Method and system for combining physiological and machine information to enhance function

ABSTRACT

The present invention relates generally and specifically to combining biological sensors with external machines using machine learning to form computerized representations that can control effectors to deliver therapy or enhance performance.

FIELD

The present invention relates generally and specifically to combiningbiological sensors with external machines using machine learning to formcomputerized representations capable of controlling effectors to delivertherapy or enhance performance. The invention integrates sensed signalsfrom body systems and artificial devices with outputs from measureablebody systems and artificial devices to create learned networks.Measurable body systems include the central and peripheral nervoussystems, cardiovascular system, respiratory system, skeletal muscles andskin well as any other body systems that are capable of producingmeasurable signals. Artificial devices include diagnostic sensors,medical stimulating or prosthetic devices and/or non-medical systems.The invention has applications in sleep and wakefulness,sleep-disordered breathing, memory and cognition, monitoring andresponding to obesity or heart failure and other conditions, or moregenerally in enhancing performance via external devices. This disclosureoutlines several applications of this invention, using as an examplemethods and systems to enhance sleep-related bodily functions for use innormal individuals or patients with sleep-breathing disorders.

This application incorporates by reference the entire subject matter andapplication of attorney docket #2480-2 PCT (application PCT/US15/46819,filed Aug. 25, 2015).

BRIEF DISCUSSION OF RELATED ART

The human body has long been interfaced with artificial devices ormachines. Prosthetic limbs have for centuries been made of wood combinedwith metal and other materials. Through recent technological advances,devices often now have sophisticated materials, design and control for aspecific purpose—such as a robotic limb (see for instancehttp://singularityhub.com/2013/07/24/darpas-brain-controlled-prosthetic-arm-and-a-bionic-hand-that-can-touch)or a glucose-sensing insulin infusion pump.

Many of these functions are mediated by the brain (central nervoussystem) and/or peripheral nervous system. These functions includeclassical “neurological” functions such as vision or hearing, but alsonearly all activities of daily life, including learning, moving, oroperating machinery.

In many situations, the body's ability to perform such functions isconstrained. Constraint can take many forms and may be physical orfunctional. Physical constraints include an external obstacle preventingmovement of a limb in an enclosed space such as may affect a warrior orscuba diver. A physical constraint may also be internal, such as loss ofa limb from amputation. Functional constraints may include a classicaldisease, such as stroke that prevents an individual's ability to movethe foot. However, functional constraints may also includeunderperformance on a task due to insufficient training, knowledge oracquisition of skills, or through disuse.

What is lacking is how devices can be used to automatically(“intelligently”) tailor therapy to restore lost physiological functionor enhance an existing function in a specific individual. This inabilityfor prior and current devices to automatically tailor therapy andrestore or enhance a function is striking when examining how the humanbrain senses, integrates and controls bodily functions.

The prior art has extensively studied, yet imprecisely defined, whichregions of the brain control bodily functions and how they interact withother physiological components in a network. Simple bodily functions,such as moving the biceps of the left arm or sensing from the rightindex finger, are well defined and often conserved between individuals.Functional mapping or “atlases” are debated even for some “simplesensations” such as visual recognition of a face. Moreover, other bodilyfunctions including “higher cortical” functions are neither well definednor conserved. This includes sleep, cognition, memory, mood, alertness,sensory-motor and many other activities.

Currently, machines that modulate bodily function are largely based on aprecise detailed knowledge of physiology, which for the brain wouldinclude neuroimaging, brain mapping and peripheral nerve mapping ofnormal and abnormal functions. Unfortunately, such detailed knowledge isoften incomplete. Mapping of functional locations often vary betweenindividuals—and even in the same person at different times. Manyfunctions are poorly mapped, such as memory, cognition and mentalperformance. Even for well mapped functions, studies that definephysiological function often raise additional uncertainties in thisfunctionality.

Mapping functional domains of a bodily function—the network ofphysiological systems associated with that function including sensedsignals and biological effectors that control it—is difficult. Mappingof functional domains is particularly difficult for functions involvingthe brain. However, there is an urgent need to sense and modulatefunctional domains whose altered function may cause disease orsuboptimal performance.

In traditional theory, sleep and wakefulness are modulated by brainregions including the posterior hypothalamus, while memory is encoded bythe hippocampus and other regions of the limbic system. However, it isnot clear what brain regions are responsible for controlling sleep, orfor mediating abnormal breathing in central sleep apnea. Regions of thebrainstem that control airway muscles are better characterized, such asnuclei in the medulla oblongata for the hypoglossal nerve (twelfthcranial nerve) that controls tongue movement. Yet, how nuclei areintegrated into abnormal breathing to produce obstructive sleep apnea isnot understood. As a result, it has been difficult to treat thiscondition even using novel systems that activate tongue motion to reduceobstruction.

Sleep is a bodily function that integrates the nervous system, skeletalmuscle, cardiopulmonary, and other body systems. Sleep alternates withand enables subsequent wakefulness, and is required for normalfunctioning of most organ systems. Sleep is traditionally considered tobe controlled by specific regions of the brainstem (primitive brain)that both regulate and are modulated by function of the higher brain(cerebral cortex), muscles controlling breathing, other involuntarymuscles such as sphincters of the gastrointestinal and genitourinarytracts, voluntary muscles such as muscles of the legs or arms, sensoryfunction, and other bodily functions.

Much work over several decades has strived to define which regions ofthe brain subtend bodily functions such as sleep. As outlined above,while functional mapping is well defined for “simple” functions such ascontrolling a defined muscle (e.g., the biceps of the upper arm) orsensation (e.g., the right index finger), it is far less clear forsleep. Interactions between the multiple organ systems impacted by sleepfurther complicate precise mapping.

An individual's ability to sleep may be compromised in many ways. Amongthe most important and common are sleep hypopnea (reduced breathing) andapnea (absence of breathing), in which impaired breathing in sleepinterrupts sleep functioning, and primary sleep disorders such asinsomnia, where the individual cannot sleep efficiently or sufficiently.All sleep disorders negatively impact wakefulness, producing daytimedrowsiness that impairs daily activities. Sleep disorders can also leadto disorders from breathing such as low oxygen levels with metaboliceffects including acidosis, disorders of the heart such as failure andabnormal rhythms, disorders of the immune system causing susceptibilityto infection, psychological disorders such as stress, depression,anxiousness and psychosis, and several other states of poor functioningand disease.

Sleep apnea may be obstructive or central. Obstructive sleep apnea isincreasingly recognized in individuals who snore, who are overweight andwho may develop sequelae such as heart failure. Central sleep apnea isalso common, yet is under-recognized and associated with comorbiditiessuch as heart failure. It is likely that central sleep apnea also occursalongside obstructive sleep apnea, since treatments that physically openthe throat muscles and prevent obstruction may sometimes leave residualapnea.

Obstructive sleep apnea (OSA) results from complete or partial airwaycollapse in sleep. Conversely, central sleep apnea (CSA) results fromreduced brain stimulation of the respiratory muscles in sleep. Bothforms are typically diagnosed using overnight polysomnography (PSG), atest that measures at least eight (8) channels including theelectroencephalogram (EEG), electrooculogram (EOG), electrocardiogram(ECG), chin electromyogram (EMG), airflow, respiratory “effort,” oxygensaturation (SaO₂ or sat), and body position. However, this is acumbersome test typically performed with an overnight hospital stayattended by physicians, is not well liked by patients, cannot easily berepeated to assess the impact of therapy and is difficult to perform athome. Recent studies have shown that commercial tests offered tocircumvent traditional polysomnography are suboptimal at best.

From a polysomnogram, apnea is defined as absence of breathing (no tidalvolume) for at least 10 seconds, while hypopnea is defined as decreasein tidal volume of 30% for at least 10 seconds accompanied by at least a3% decrease in oxygen saturation or terminated by arousal from sleep.Apnea is defined as obstructive if accompanied by inspiratory effortagainst the occluded pharynx. Without such accompanying effort, apnea isdefined as central. Similarly, hypopnea is obstructive if there aresigns of upper airway flow limitation, and is otherwise consideredcentral. The apnea-hypopnea index (AHI) is the ratio ofapnea-to-hypopnea per hour of sleep, and is classified as no sleep apnea(AHI<5), mild sleep apnea (AHI of 5-15), moderate sleep apnea (AHI of15-30) or severe sleep apnea (AHI>30).

Several treatments are available for obstructive sleep apnea, but theseare often not well tolerated. The most commonly used treatment currentlyis continuous positive airway pressure to keep the airway open andreduce/eliminate obstruction. Other options include mechanical splintsand even surgical procedures to reduce/eliminate obstruction. Somerecent devices have applied stimulation to the muscles of the tongue orface to eliminate obstruction, but it is unclear how well they will workin the broad population.

Few strategies have been proposed to improve central sleep apnea—or moregenerally the central control of sleep. Since central sleep apnea mayrelate directly to sleep disorders, treatments for central sleep apneamay potentially also help other conditions. It is increasinglyappreciated that central sleep apnea makes heart failure worse, and sotreatment for central sleep apnea may improve symptoms of heart failure,and other cardiac and non-cardiac conditions such as insomnia andpsychological sequelae.

Pharmacological drug therapy is often used to induce sleep, but theseagents are not useful in sleep apnea. These drugs rarely mimic thenatural stages of sleep, rarely induce rapid eye movement (REM) sleepthat is essential for restfulness, and may paradoxically worsen sleepdisorders and produce daytime drowsiness despite nighttimeunconsciousness.

New therapeutic modalities are clearly needed to modulate the complexfunctions outlined above—often including a component of central orperipheral nervous system involvement. Emerging modalities involveelectrical stimulation/modulation of brain or nervous system activity,typically at a specific target region. All these current modalitiessuffer from a significant common problem, as they attempt to performtherapy with no or minimal sensory input, feedback, or modulation ofsuch therapy based upon the individual patient's neurological activity.

One example of electrical stimulation therapy is noninvasive orminimally invasive trigeminal nerve stimulation (e.g., NEUROSIGMA®) thatis being evaluated to treat depression and seizures. Unfortunately, thetrue mechanism of action of such therapy is unclear. Whether this is dueto the actual trigeminal nerve being stimulated, direct stimulation ofthe frontal lobe of the brain, indirect inhibition of cerebral bloodflow or some other as yet unknown mechanism, still remains to bedetermined and will affect the ability of such therapy to be appliedsuccessfully. Additionally, this therapy is applied as a“one-size-fits-all” approach without any adaptation for individualpatient responses.

Other non-invasive neuromodulation/stimulation approaches are also beingconsidered include stimulation of the vagus nerve for seizures(Carbomed, Inc.). Similar to trigeminal nerve stimulation, the mechanismis poorly understood, the actual stimulation of the vagus nerve isunclear via this noninvasive approach, and there is no individualpatient adaptation. A number of technologies are attempting to treatdepression via noninvasive transcranial application of an electricaland/or magnetic field (Neuronetics Inc., Neosync Inc., Brainsway Inc.,Cervel Neurotech Inc., and Tal Medical Inc.). All of these approaches,even though they show interesting preliminary data suffer, from the sameproblems as above, namely, poor understanding of mechanism and lack ofpatient-tailored therapy due to a lack of feedback and adaptation forindividual patients.

For apnea specifically, approaches that try to modulate obstructivesleep apnea, including stimulation of the hypoglossal nerve (Inspire MedInc.) or other throat muscle (Apnex Medical Inc.)—are being evaluatedbut typically do not have individual patient-tailored therapies. Infact, whether direct management of the obstruction resolves the problemof apnea is also unclear due to commonality of a central sleep apneacomponent in most patients.

Invasive approaches to neuromodulation include vagal nerve stimulationto treat seizures and depression (Cyberonics), spinal cord stimulationto treat pain (such as Medtronic Inc., Boston Scientific Inc., AdvancedNeuromed Inc.), direct deep brain stimulation to treat seizures(Medtronic Inc., Boston Scientific Inc.) or even cognitive disorders(Thync Inc.). However, these therapies target single components of thephysiologic network for a bodily function, and are limited because theydo not consider the remaining network. This may lead to suboptimaltherapy, compensatory mechanisms that further diminish the efficacy oftherapy, or unwanted effects. Moreover, these therapies are only as goodas the accuracy of their specific targets, and brain/nerve regions areimprecisely defined for many bodily functions including sleep control,sleep-breathing conditions, cognition, alertness, memory, overall mentalperformance, or response to obesity.

Traditional therapies have also not typically been effective formanaging central sleep apnea, other cognitive or performance functions,alertness, heart failure or obesity.

When devices are used for other functions, such as the increasing use ofvirtual environments, the goal is usually to create an illusionary orrepresentative environment by feeding specific sensory inputs (primarilyvisual, tactile and/or auditory) to replicate existing real-worldexperiences. Unfortunately, such approaches may be limited in thatnormal pathways vary from individual to individual. Thus, simulatingnormal often may not accurately replicate that function for anindividual nor represent normal for that individual.

In other situations, the use of devices to enhance or compensate forother functions such as motor tasks are limited or constrained.Constraint can take many forms and may be physical or functional.Physical constraints include an external obstacle preventing movement ofa limb in an enclosed space such as may affect a warrior or scuba diver.A physical constraint may also be internal, such as loss of a limb fromamputation. Functional constraints may include a classical disease, suchas stroke that prevents an individual's ability to move the foot.However, functional constraints may also include underperformance on atask due to insufficient training, knowledge or acquisition of skills,or through disuse.

Many attempts have been made to address these constraints, using afamiliar paradigm that body sensors (e.g., the eye), nervous function(e.g., the central and peripheral nervous system) and effector organs(e.g., a muscle group) can often be functionally mapped to specificanatomic locations. However, the precise locations of the brain or otherphysiological systems that control each task are not well defined. Suchfunctional maps or “atlases” are often debated for complex functions.Much data has come from animal models that are not well suited to modelor analyze complex human functions or mental functions.

It would be of great benefit to society to develop a device to enhancebodily functions by modulating its interfaced functional components. Forinstance, a device to restore sleep functionality, i.e., to prevent ortreat central sleep disorders, would be of great value. Such devices mayimprove daytime performance in individuals without disease, or reducesymptoms in patients with disorders associated with central sleep apneasuch as heart failure. It would also be of immense benefit to constructa device able to restore/enhance other functions such as motor activityor even some aspects of neural functioning without the need to defineprecise physiological, neural or other pathways to guide therapy.Currently, there are few methods in the prior art to achieve thesegoals.

SUMMARY OF THE PRESENT INVENTION

The invention is designed to monitor and modulate a complex bodilyfunction using a combined biological and machine approach. Unlike theprior art, the current invention uses machine learning to derive arobust relationship between sensed signatures of measurable body systemsand bodily functions in animals and in particular human beings, but doesnot require presumed physiological relationships or mechanisms. Theinvention is then able to enhance performance of function or re-instatelost functions using this robust relationship or enciphered functionalnetwork.

For the purposes of this disclosure, the following definitions apply.

Associative learning is defined as the process of linking sensedsignatures and other inputs with a body task. For this disclosure, bodytasks are typically complex tasks rather than reflexive or other simpletasks. Associative learning may be iterative, such that associations aremodified (“learned”) based upon patterns of change between theseprocesses. For the purposes of this disclosure, this may be associatinglow impedance with abnormal breathing.

Bodily function is defined as the processes needed to perform a task,that may include physiologic or pathological processes. Examples includesleep, sleep apnea, mental performance, or the response to obesity.Bodily functions involve a network of several functional domains thatoften interact including the brain and central nervous system,peripheral nervous system, cardiovascular, pulmonary, gastrointestinal,genitourinary, immune, skin and other systems. A bodily function mayresult from biological activity/function, and may involve anon-biological or artificial component, e.g., reading with glasses,driving, using remote control unit, a patient moving a combinednatural/cybernetic limb, etc.

Bodily signal means signals generated by and/or sensed from a human,animal, plant, bacterial or other single-cell-based body ormulti-cell-based body. For purposes of this definition, viruses andprions are included. Bodily signals particularly include signalsgenerated by and/or sensed from the human body. Bodily signals aregenerally associated with bodily functions. The term “non-bodily signal”indicates that it is generated from a source other than a single- ormulti-cell-based body. Examples include an external “signal” from anexternal electrical source, machine, sensor, etc. When the term “signal”is used without the term “bodily” or “non-bodily”, the term “signal”indicates that it includes both “bodily signals” and “non-bodily”signals, i.e., it includes all signals.

Body means the physical structure of a single-celled organism, amulti-celled organism, viruses and prions. Organisms include animals(such as, but not limited to, humans), plants, bacteria, etc.

Effector is a means of performing a bodily task, and may include aphysical effector such as for moving a limb or moving the diaphragm toenhance breathing during sleep, or an artificial effector such as acybernetic limb or electrical stimulation to complete a task.

Effector response is the result of the effector, which may or may notcomplete a bodily task. For instance, if the effector is the tricepsmuscle in the arm, an effector response is to extend the arm by 30degrees, while the entire task may be to fully straighten the arm.

Effector signal is the signal delivered by the invention to the effectorto produce the effector response.

Functional domain is the aggregation of all the elements relating to adistinct bodily function, sometimes associated with a specific organsystem or a combination of systems that results in the overall function,e.g., breathing. For a simple function, this may reduce to a sensed“dermatomal distribution”, for instance sensation at the shoulder ismediated by sensory nerves from the C435 distribution of the spinalcord. However, even such simple domains are more complex (andnetworked), in that shoulder sensation is mediated by nerves that alsosupply the heart. Functional domains include nerves, blood vessels, thelymphatic system, interstitial tissue planes and hormonal centers.Sensed signatures are measurable physiological parameters or indices,used individually or in combination from body systems above, that arelinked with a body function and in aggregate describe that function.

Other definitions include a biological function, which means anyfunction that is the direct result of natural biological activity suchas breathing, heart beating, walking, running, sleeping, dreaming and soon.

A symbolic model herein is a mathematical representation of a function,linking measured sensed activity with a task even if completephysiological description for that task are lacking. It can also betermed a symbolic representation. This may include analog recordedphysiological signals, digital coded ciphers, computer code, visualrepresentations such as photographs or graphics, and so on, and can beused to aid in rapid, clear transformation to perform a specifiedmethod. Associative learning is an iterative process of linkingprocesses, typically including sensed signatures and complex biologicaltasks, and modifying these associations (“learning”) based upon patternsof change between these processes (for instance, associating lowimpedance with abnormal breathing).

Enciphered network or enciphered functional network (EFN) is defined asa model associating measured parameters (sensed signatures) with aspectsof the bodily task including effectors and other sensors. This enablesmonitoring and improved functionality of that body function. Theenciphered network is designed to parameterize a functional domain, forexample it links sensed activity with a task even if completephysiological or mechanistic description for that task is lacking. Thisdeparts from the traditional approach of meticulously mapping orrecapitulating functions in each biological organ system. The networkcan be symbolic in the form of a symbolic representation such assymbolic code, in which case it may be a mathematical or other abstractrepresentation. If applied to the nervous system, this can be termed anenciphered nervous system.

Encipher is defined as the process of coding information.

Enhanced performance or enhancement is defined as improvement to thenormal healthy and non-diseased baseline function in an individual.Enhanced performance thus would not include therapies for disease suchas pacing in an individual with abnormally slow heart rates or in apatient or an insulin pump in known diabetic patients.

External machine is defined as a mechanical, electrical, computationalor other non-natural (native biological) device. This may be external tothe body but can be in contact with or implanted within the body.

Extremity of the body is defined as limbs and associated structures ofthe body including arms, legs, hands, feet, fingers, toes, andsubsegments thereof.

Functional domain is defined as the elements relating to a bodily task.This may include sensed elements, analysis elements and effectorelements. Analysis elements may be “learned”, preprogrammed, reflexive,or passive. Each element may be biological, non-biological orartificial.

For example, a functional domain may sometimes be associated with aspecific organ system or a combination of systems that results in theoverall function, e.g., breathing. For a simple function, this mayreduce to a sensed “dermatomal distribution”, for instance sensation atthe shoulder is mediated by sensory nerves from the C435 distribution ofthe spinal cord. However, even simple domains may be more complex, inthat shoulder sensation is mediated by nerves that also supply theheart. Functional domains thus include nerves, blood vessels, thelymphatic system, interstitial tissue planes and hormonal organs. Sensedsignatures are measurable physiological parameters or indices from theseorgan systems, used individually or in combination that are associatedwith a body function and in aggregate describe that function.

Functionally associated is defined as sensed signals or functionaldomains that occur together when that function occurs. An example wouldbe activity in portions of the brain controlling breathing with activityin muscles of breathing such as the intercostal muscles or diaphragm.Functional association does not need to follow biological pathways. Forexample, a functional association includes sensed activity in shouldernerves with heart related problems such as angina, in which shouldernerve activity is not part of the biological processes causing heartproblems.

Machine learning is defined as a series of analytic methods andalgorithms that can learn from and make predictions on data by buildinga model rather than following strictly static programming instructions.These machine learning approaches “learn” patterns and functions with atleast some components that are not preprogrammed (i.e., instructed). Inthis sense, machine learning creates individualized solutions ratherthan generic ones. Machine learning can take many forms, includingartificial neural networks, heuristics, deterministic rules and combinedapproaches.

Sensed signatures are defined as one or more signals from sensorsrelated to a bodily task. Sensors may be biological, non-biological orartificial. Sensed signatures are inputs of the functional domain.Sensed signatures can be physiological parameters such as nerve firingrates and oxygenation level, that are associated with the function inquestion such as sleep disordered breathing.

Mental alertness is defined as an awake state that focuses on a specificdesired task, that can be measured by performance at that task. Improvedmental alertness is characterized by being awake and performing mentaland other tasks well. Reduced mental alertness can include many statesthat include but are not limited to impaired performance of a task,“mental fatigue”, loss of focus, attention deficit, somnolescence,sleepiness, narcolepsy, sleep and disease processes that include theabove as well as coma, “fugue” state and others.

“Task” means a piece of work, action or movement to be done, completedor undertaken. The term “bodily task” means a piece of work, action ormovement to be done, completed or undertaken by a “body”, definedherein.

Therapeutically effective is defined as an effector function or dose ofan intervention or therapy that produces measurable improvement in oneor more patient outcomes. An example would be patterns of energydirected to the scalp to stimulate target regions controlling breathing,in order to treat central sleep apnea. Ideally, an intervention willminimize impact to other regions of the body, in this case the scalpwhich may be achieved by a small contact device rather than a cap thatencompasses the entire scalp, or focusing energy from a non-contactdevice on the target region and not the entire head.

Other biological terms take their standard definitions, such as heartfailure, tidal volume, sleep apnea, obesity and so on.

This invention creates an enciphered functional network. The potentialnumber of uses of this invention are broad.

In one aspect, there is provided a method for interacting with the humanbody, the method including detecting bodily signals associated with oneor more bodily functions at one or more sensors associated with thehuman body, processing the bodily signals to create one or more sensedsignatures of the one of more bodily functions, processing thesignatures using an enciphered functional network utilizing machinelearning to determine one or more effector responses needed to control abodily task, delivering via the enciphered functional network one ormore effector signals (the effector signals based on the one or moreeffector responses), and controlling a bodily task.

In another aspect, there is provided a method to enhance performance ofa bodily task, the method including detecting signals associated withthe task at one or more sensors, processing the signals to create one ormore sensed signatures, processing the signatures using an encipheredfunctional network to determine one or more effector responses needed toenhance performance of the bodily task, delivering via the encipheredfunctional network one or more effector signals (the effector signalsbased on the one or more effector responses), and enhancing performanceof the task.

In another aspect, there is provided a method for treating a disease,the method including detecting signals associated with one or morebodily functions at one or more sensors associated with the human body,processing the signals to create one or more sensed signatures of theone of more bodily functions, processing the signatures using anenciphered functional network utilizing machine learning to determineone or more effector responses needed to treat a disease, delivering viathe enciphered functional network one or more effector signals (theeffector signals based on the one or more effector responses), andtreating the disease.

In another aspect, there is provided a method for transforming nerveactivity associated with one or more bodily functions, the methodincluding detecting bodily signals of nerve activity associated with theone or more bodily functions at one or more sensors, processing thebodily signals to create one or more sensed signatures of the one ormore bodily functions, processing the signatures using an encipheredfunctional network utilizing machine learning to determine one or moreeffector responses needed to transform nerve activity, delivering viathe enciphered functional network one or more effector signals (theeffector signals based on the one or more effector responses), andtransforming nerve activity.

In another aspect, there is provided a method for controlling a deviceusing an enciphered functional network, the method including detectingbodily signals from a body using one or more sensors, processing thebodily signals to create a sensed signature, processing the sensedsignature using an enciphered functional network utilizing machinelearning to determine one or more effector responses to control thedevice, delivering via the enciphered functional network one or moreeffector signals (the effector signals based on the one or more effectorresponses), and controlling the device.

In another aspect, there is provided a method to measure bodily functionin an animal, the method including detecting bodily signals associatedwith sensory activation, processing the bodily signals to create one ormore sensed signatures, and processing the sensed signatures using anenciphered functional network to determine one or more effectorresponses needed to enhance the bodily function of the animal.

In another aspect, there is provided a method of improving a specifichuman performance, the method including identifying one or more regionsof a human body associated with parts of the brain that serve a specificfunction, placing low energy stimulating electrodes proximate to the oneor more regions of the human body, applying stimulation through theelectrodes to activate the parts of the brain, and measuring changesrelated to the parts of the brain to verify improvement of the specifichuman performance.

In another aspect, there is provided a method for treating a sleepdisorder, the method including selecting one or more regions of apatient's central nervous system and/or peripheral nervous systemassociated with sleep functioning, and applying low energy stimulationthrough electrodes to activate the patient's one or more regions ofcentral nervous system and/or peripheral nervous system to treat thesleep disorder.

In another aspect, there is provided a method of enhancing attention,the method including selecting one or more regions of a patient'scentral nervous system and/or peripheral nervous system associated withan attention disorder, and applying low energy stimulation throughelectrodes to activate parts of a patient's central nervous systemand/or peripheral nervous system to treat the attention disorder.

In another aspect, there is provided a method of treating central sleepapnea, the method including identifying a target region from one or morelocal areas of the head and neck (the target region being functionallyassociated with one or parts of the brain that control sleep), anddelivering a therapeutically effective amount of energy to stimulate thetarget region to treat the central sleep apnea, while minimizingstimulation of other regions of the body.

In another aspect, there is provided a method of modulating mentalfunction, the method including identifying a target region selected fromlocalized areas of the body (the target region being functionallyassociated with parts of the brain that govern the mental function), themental function including one or more of alertness, cognition, memory,mood, attention and awareness, and delivering a therapeuticallyeffective amount of energy to stimulate the target region to modulatethe mental function, while minimizing stimulation of other regions ofthe body.

In another aspect, there is provided a system for interacting with thehuman body, the system including a processor and a memory storinginstructions that, when executed by the processor, performs operationsincluding detecting bodily signals associated with one or more bodilyfunctions at one or more sensors associated with the human body,processing the bodily signals to create one or more sensed signatures ofthe one of more bodily functions, processing the signatures using anenciphered functional network utilizing machine learning to determineone or more effector responses needed to control a bodily task,delivering via the enciphered functional network one or more effectorsignals (the effector signals based on the one or more effectorresponses), and controlling a bodily task.

In another aspect, there is provided a system to enhance performance ofone or more tasks, the system including a processor and a memory storinginstructions that, when executed by the processor, performs operationsincluding detecting signals associated with the task at one or moresensors, processing the signals to create one or more sensed signatures,processing the signatures using an enciphered functional network todetermine one or more effector responses needed to enhance performanceof the bodily task, delivering via the enciphered functional network oneor more effector signals (the effector signals based on the one or moreeffector responses), and enhancing performance of the task.

In another aspect, there is provided a system to treat a disease, thesystem including a processor and a memory storing instructions that,when executed by the processor, performs operations including detectingbodily signals associated with one or more bodily functions at one ormore sensors associated with the human body, processing the bodilysignals to create one or more sensed signatures of the one of morebodily functions, processing the signatures using an encipheredfunctional network utilizing machine learning to determine one or moreeffector responses needed to treat a disease, delivering via theenciphered functional network one or more effector signals (the effectorsignals based on the one or more effector responses), and treating thedisease.

In another aspect, there is provided a system to transform nerveactivity associated with one or more biological functions, the systemincluding a processor and a memory storing instructions that, whenexecuted by the processor, performs operations including detectingbodily signals of nerve activity associated with the one or more bodilyfunctions at one or more sensors, processing the bodily signals tocreate one or more sensed signatures of the one or more bodilyfunctions, processing the signatures using an enciphered functionalnetwork utilizing machine learning to transform nerve activity,delivering via the enciphered functional network one or more effectorsignals (the effector signals based on the one or more effectorresponses), and transforming nerve activity.

In another aspect, there is provided a system to control a device usingbiological signals, the system including a processor and a memorystoring instructions that, when executed by the processor, performsoperations including detecting bodily signals from a body using one ormore sensors, processing the bodily signals to create a sensedsignature, processing the sensed signature using an encipheredfunctional network utilizing machine learning to determine one or moreeffector responses to control the device, delivering via the encipheredfunctional network one or more effector signals (the effector signalsbased on the one or more effector responses), and controlling thedevice.

In another aspect, there is provided a system to measure visual functionin an animal, the system including a processor and a memory storinginstructions that, when executed by the processor, performs operationsincluding detecting bodily signals associated with sensory activation,processing the bodily signals to create one or more sensed signaturesrepresenting quantitative measures of sensation, and processing thesensed signatures using an enciphered functional network utilizingmachine learning to determine one or more effector responses needed toenhance the bodily function of the animal.

In another aspect, there is provided a system for improving a specifichuman performance, the system including a processor and a memory storinginstructions that, when executed by the processor, performs operationsincluding identifying one or more regions of a human body associatedwith parts of the brain that serve a specific function, placing lowenergy stimulating electrodes proximate to the one or more regions ofthe human body, applying stimulation through the electrodes to activatethe parts of the brain, and measuring changes related to the parts ofthe brain to verify improvement of the specific human performance.

In another aspect, there is provided a system for treating a sleepdisorder, the system including a processor and a memory storinginstructions that, when executed by the processor, performs operationsincluding selecting one or more regions of a patient's central nervoussystem and/or peripheral nervous system associated with sleepfunctioning, and applying low energy stimulation through electrodes toactivate the patient's one or more regions of central nervous systemand/or peripheral nervous system to treat the sleep disorder.

In another aspect, there is provided a system to enhance attention, thesystem including a processor and a memory storing instructions that,when executed by the processor, performs operations including selectingone or more regions of a patient's central nervous system and/orperipheral nervous system associated with an attention disorder, andapplying low energy stimulation through electrodes to activate parts ofa patient's central nervous system and/or peripheral nervous system totreat the attention disorder.

In another aspect, there is provided a system to treat central sleepapnea, the system including a processor and a memory storinginstructions that, when executed by the processor, performs operationsincluding identifying a target region from one or more local areas ofthe head and neck (the target region being functionally associated withone or more parts of the brain that control sleep), and delivering atherapeutically effective amount of energy to stimulate the targetregion to treat the central sleep apnea, while minimizing stimulation ofother regions of the body.

In another aspect, there is provided a system to modulate mentalfunction, the system including a processor and a memory storinginstructions that, when executed by the processor, performs operationsincluding identifying a target region selected from localized areas ofthe body (the target region being functionally associated with parts ofthe brain that govern the mental function, including one or more ofalertness, cognition, memory, mood, attention and awareness), anddelivering a therapeutically effective amount of energy to stimulate thetarget region to modulate the mental function, while minimizingstimulation of other regions of the body.

One motivation for this invention is that detailed deterministicsolutions for many complex bodily functions are inherently limited fortherapy. This reflects several factors. First, there is inter-individualvariation in regions of control—for instance, the biological neuralnetwork to talk in one person differs from the biological neural networkto talk in another. For functions with a nervous component, this mayrepresent the unique fashion in which higher cognitive functions andmemories are shaped during growth and development in each person orpotentially genetically established. Secondly, many brain functions areplastic—changes in the environment or disease can alter control regions.Changes can be gradual or abrupt, causing variations over years, monthsor even weeks that may reflect normal development, aging or dysfunction.This may explain why traditional therapies that are initially effectivebecome ineffective over time. Thirdly, our conceptual knowledge offunctional domains in the central and peripheral nervous system is inits infancy. It is thus a major challenge to understand a bodilyfunction using a classical paradigm of observing then stimulating aspecific target to map its region(s) of control.

Several innovations separate this invention from the prior art. First,the invention creates an enciphered network for the bodily function.This reconstructs the function as network of functional domains. Thisdeparts from the traditional approach of meticulously mapping orrecapitulating each biological organ system. Instead, second, itidentifies sensed signatures and effectors for each function. Signaturescan be nervous or non-nervous system related. Third, the inventionapplies a feedback loop to apply measured and quantitatively determinedtherapy that may change over time even in the same individual. This isinherent because the enciphered network can be trained over time usingan ongoing machine learning processes. Fourth, the core logic of theinvention is patient-tailored, distinct from the majority of currentdevices that use “one-size-fits-all”, generic or stereotypic therapy.Fifth, therapy is adaptive through continued machine learning, such thata similar abnormality in the same individual may produce differentsignatures and/or require different effector responses at two or moredistinct periods in time. Sixth, certain embodiments of the devicecombine biological and non-biologic devices, together or individually.The enciphered representation can accommodate novel signatures overtime, that can be extrinsic artificial signals as well as intrinsicbiological ones. Therapy can ultimately be delivered by an externaldevice and/or by direct stimulation or inhibition of an effector.Embodiments include improvements of sleep apnea, the body's response toheart failure, obesity, alertness, memory and mental performance orcognition.

The concept of accessing functional domains for a task by measuring fromor stimulating an interconnected region of the network, that may beneural, vascular or other, is novel at several levels and has not beenaddressed by devices in the prior art. One example way to betterunderstand this concept is by considering the disease of central sleepapnea.

The functional domain for central sleep apnea in this invention includessensed signatures of brain function (measurable on the EEG), reducedoxygenation levels and increased carbon monoxide levels in the blood(measurable from skin sensors), and in some individuals increased heartrate and altered patterns of heart rate and other less definedfunctions. Observed but unexplained signatures may include nocturnalrostral fluid shift from the legs (that may link sleep apnea with heartfailure). Effector responses for central sleep apnea is to nervefunction to the neck muscles, diaphragm, intercostal muscles andaccessory muscles (measurable by nerve firing rates). The presentinvention will use these sensed signatures of brain or nerve activity,chest wall movement, bioimpedance at the skin (to assess for a rostralfall), or oxygenation for diagnosis and monitoring. In an embodiment fortreatment, the invention may result in varying effector responses.

Chest wall impedance can be expressed in many forms. In this invention,the sensed signature of abnormal chest wall impedance includes a ratioof lower body impedance (e.g., leg, lower back) to higher body impedance(neck and chest)—i.e., higher impedance in lower body (lessextracellular water), lower impedance in upper body (more extracellularwater). This could also be expressed as upper-to-lower body conductance.This could also include measuring impedance to different forms,patterns, or waveforms of electrical energy.

In one preferred embodiment, machine learning is used to associaterepeatedly measured signatures with normal breathing. If apnea arisesduring sleep, the invention will apply tailored therapy adaptively toalter domain activity and alleviate sleep apnea. The response to therapy(e.g., effector response) can also be assessed repeatedly via sensedsignatures, and the therapy can be withdrawn or continued based uponthese signatures. This differs from the prior art in which therapiessuch as continous positive airway pressure or nerve stimulation areoften delivered empirically, continuously or in predetermined fashionswithout adaptive algorithms to tailor therapy.

Other sensed signatures include altered nerve firing rates for mentalperformance or sleep, vasodilation during sleep, reduced skin galvanicresistance (from altered electrolytes or edema) in the body's responseto heart failure or sleep-breathing disorders, altered skin absorptionor emission of near-infrared or other components of the electromagneticspectrum during sleep disorders, measured alterations to other forms ofapplied non-electrical energy including optical signals (alteredreflectance), sound or ultrasound (different sonic reflectance andscattering), and potentially altered spectroscopic signals of bodychemistry that can be sensed.

The network of functional domains is a unique approach for interfacingwith bodily functions. For instance, a patient with heart pain (anginapectoris) or a heart attack (myocardial infarction) often experiences“radiated pain” to the left arm, shoulder or other regions. Somepatients experience only arm pain from cardiac ischemia—i.e., arm painis a sensed signature. This signature may not be relevant to otherindividuals a priori—but can be learned by the enciphered network forthat individual. In this way, the invention can now detect nerveactivity in the arm below the typical nerve firing rates for sensed“pain”, providing the device with an early warning sensor for heart pain(“angina”) to provide therapy or alert medical personnel.

In another example, patients with problems of the abdominal viscera(stomach, small intestine, large intestine) that may include normal“indigestion” as well as diseases often experience vague discomfort onthe abdominal wall through imprecisely defined and variable visceral andsomatic nerves. Massaging this region is an example ofcounter-stimulation that can alleviate the visceral organ pain. In oneembodiment, the invention will thus provide algorithmically determinedvibratory stimulation to appropriate skin regions within the “functionaldomain” of the bodily function to alleviate pain.

As yet another example, nerve firing in cutaneous or other accessiblenerves (e.g., mucous membranes of mouth, anus, or skin of the externalauditory meatus) may share neural control regions with other organs,such as heart pain or even abnormal heart rhythms. Effector signals canbe delivered to specific regions of the functional domain to alleviateheart pain or other abnormalities. Other components of a functionaldomain may include blood vessel flow, vasomotor reactivity, skinelectrical conductivity, heart rate or heart rate variations, breathingrate, cellular edema and other indices illustrated throughout thespecification.

Therapy is individually tailored and not empirically delivered. Baselinesignatures such as rates and patterns of nerve firing during a desiredlevel of functioning are analyzed and learned in each individual and maybe combined with other signatures within the enciphered functionalnetwork. In states such as sleep-disordered breathing, heart failure,fatigue and others, fluctuations outside this normal range are detectedand can be used to monitor disease or performance. Therapy such asstimulating neck muscles for obstructive sleep apnea, stimulatingaccessory muscles or alertness centers for central apnea, or therapy forheart failure and other conditions can be monitored (e.g., by effectorresponse) and tailored to machine learned signatures. Functionality canthus be modulated without direct knowledge or access to the primaryphysiological target and without detailed pathophysiological knowledgeof that function.

Nerve signatures may be shared between many functions, e.g., based ondermatomal distributions of peripheral nerves. One example is sensationof the tip of shoulder blade at the “C234” region, control of deltoidmuscle function by the “C56” region, and control of the diaphragmmuscles and hence breathing at the “C345” region. Thus, sensation at theshoulder can indicate shoulder stimulation, or pain in portions of theheart adjacent to the diaphragm. Stimulation at these regions by directelectrical stimulation, vibratory stimuli, heat or other can produce acounter-irritant to the measured function.

Brain signatures can be assessed directly via the EEG or simplified EEGmeasured from the scalp by many types of electrical sensor. Forinstance, scalp activity in the alpha (7.5-12.5 Hz), beta (12.5-30 Hz)or gamma (25-40 Hz) bands indicate states of awakeness (wakefulness) orheightened or alertness; activity in the delta (0.1-3 Hz) or theta (4-7Hz) bands indicate drowsy (or comatose) states. Depending on sensedactivity, interventions can be applied to the scalp or other domains ofthe network while monitoring alpha, beta or gamma signatures tofacilitate alertness. In each case, the invention is novel in that itderives patient-tailored signatures for a given function using machinelearning, and will apply interventions algorithmically in a tailoredfeedback loop. In one preferred embodiment this will enhance sleepfunction.

Peripheral nerve signatures are numerous. For instance, increased nervefiring of the cervical sympathetic plexus in the head and neck may beassociated with alertness or rapid eye movement (REM) sleep, and reducedactivity may be associated with drowsiness or stages I-IV of sleep.Stimulation of those regions of the head and neck can be used toincrease alertness. Increased firing of the accessory (XI), facial (VII)or other cranial nerves may indicate impending obstructive sleep apnea,and may provide targets for therapy.

There are several non-nerve domain signatures. For instance,deoxygenation of an oxygen sensor on the skin of a finger (via opticalreflectance or plethysmography) can indicate hypopnea or apnea.Increased skin temperature or blood flow (absorption in red wavelengthson an optical sensor) may occur in stages I-IV sleep fromparasympathetic activation. Novel skin sensors can detect changes inbiomarkers such as glucose (to detect diabetic states, need to eat), INR(a test of blood thickness for some patients on blood thinners) and anew generation of sensors for drugs in the blood stream, chemicalchanges on the skin and so on, Interpretation of these signatures can betroublesome but is linked in this invention by machine learning to aspecific function, e.g., fever increases skin temperature, but isaccompanied by increased breathing rate and altered skinbiochemistry/impedance (due to perspiration). By learning based onmultiple signatures, temperature information can be used in this case todistinguish changes in breathing rates due to fever from that due tocentral sleep apnea.

This invention adapts to concepts of neural plasticity. Plasticityrefers to alterations in the pathways of nerves and connections(synapses) from changes in behavior, environment, neural processes,thinking, and emotions, and also to changes resulting from injury. Thisconcept has replaced prior teachings that the brain and nervous systernaare static organs. New studies show that the brain changes in anatomy(structure) and physiology (functioning) over time. There are severalexamples, e.g., DARPA limb projects, stroke victims recovering functionafter months or years of physical or occupational therapy despite havinginfarcted the traditional brain areas for the target function.Plasticity is also observed in peripheral nerves, for instance thedistribution of a functioning nerve (dermatome) can expand into anadjacent distribution of diseased nerve supply. In other words, asingular function can be assumed or subsumed by different regions of thecentral or peripheral nervous system, that will also have non-neuralimplications, e.g., on measured blood flow, galvanic skin resistance orother physiological parameters.

This invention uses the principle that continuous machine learning willenable its functionality to be retained even when plasticity occurs, andagain without precise physiological mapping knowledge for that function.For instance, in classical Pavlovian training, dogs were trained tosalivate when exposed to non-food-related stimuli that had previouslybeen associated with food in training. In other words, a new trainedstimulus—function interaction—was used without knowledge of detailedphysiological linking for that function.

This invention also encompasses personalized learned feedback loops, tomodulate a desired bodily function by algorithmic machine learninganalogous to classical Pavlovian conditioning. In a training mode,stimulation is applied during normal periods—for instance, vibratorystimulation of the skin of the lower back on days of anticipated restfulsleep. Subsequently, if sleep is interrupted, trained modes ofstimulation are applied. This mode can be applied to various bodilyfunctions including but not limited to alertness, memory, sleep andsleep-disordered breathing.

The present invention identifies functional domains empirically, andprovides computational customized, individualized solutions. Thisdiffers from the prior art in which, for example, preferred embodimentfor sleep disordered breathing stimulate cranial nerves (e.g.,trigeminal or hypoglossal), but through unclear mechanisms that may infact inadvertently work by training certain responses or stimulatingother regions than those intended.

In another set of preferred embodiments, the enciphered network can beused to enhance body performance in non-disease states. One direction isto utilize unused body capacity. In daily western life, humans oftenunderuse torso, leg and arm sensors and effectors yet frequently use eyeand hand sensors and effectors. Stimulation of underused regions by adevice can extend the sensory capacity (bandwidth) of an individual.When combined with artificial sensors, these underused regions can alsobe used to provide a “sixth sense” (see drawings) to extend sensation tobiologically unsensed stimuli (e.g., a carbon monoxide sensor canprovide vibratory stimuli to unused portions of the body), to train thebody (e.g., improve alertness) or other function.

Enhancement of performance may require specific stimulation patternsthat vary based on frequencies, amplitudes and sites of stimulation.This information can be derived by machine learning of sensed signaturesor patterns in each individual. Another approach is to use patterns fromindividuals who are highly functioning in that desired modality—from ade-identified database, by crowd sourced data collection from wearabledevices or by other means. These patterns can then serve as inputs formachine learning algorithms in the enciphered network, that willinterface them to the symbolic representation for an individual totailor them appropriately.

Effector stimulation should avoid inadvertent recruitment of existingbodily functions by applying non-physiological or atypical physiologicalstimuli. This can be achieved by using neural frequencies or patternsthat are not part of normal processing or pathways, such as outside thenormal sensed frequency, or with a different pattern, or at a different(lower) amplitude. Using other examples in this disclosure, theinvention may detect subclinical nerve firing in the functional domainfor cardiac ischemia as an early warning for angina, or application ofsubclinical amplitudes of nerve stimulation to the accessory muscles tostimulate breathing (for central sleep apnea) or neck (to improvealertness). These safeguards will avoid invoking behavioral change,sensation by the brain and/or changing memory of an event (Redondo etal., Nature 2014).

The invention can work with several types of sensors individually or incombination. Examples include solid physical sensors such as FINEhttp://singularityhub.com/2013/07/24/darpas-brain-controlled-prosthetic-arm-and-a-bionic-hand-that-can-touch/),traditional ECG- or EEG-electrical sensors, non-solid sensors such aselectrostatic creams, sensors for bioimpedance, piezoelectric filmsensors, printed circuit sensors, photosensitive film, thermosensitivefilm, and external-oriented sensors not in contact with the body such asvideo, IR, temperature, gas sensors, as well as other sensors. Thesesensors detect stimuli and transduce the information through aconstructed/created (non standard or non-somatotopic) path to activenerves.

Processing elements include a digital signal processor to interface withoutput elements that can stimulate different parts/nerves of the body,or cause mechanical action in an external machine. Such elements couldinclude traditional computing machines with integrated circuits inisolation or networked (e.g., cloud computing), biological computing,integrated biological/artificial devices (cybernetic) or utilizingunused biological capacity to perform specific, directed tasks. Onepotential embodiment is to use unused computational capacity of thecentral nervous system to perform pattern recognition in lieu ofprogramming an artificial computer for this purpose. This can beaccomplished by training an individual to recognize avisual/auditory/olfactory or other sensation and then sensing the sensedsignature of that evoked response when that stimulus is subsequentlyencountered.

Effector elements can include direct electrical outputs, piezoelectricaldevices, visual/infrared or other stimulatory systems, nerve stimulatingelectrodes or servo motors to control a limb, digitized electronicsignals such as radiofrequency or infrared transmissions, or evenvirtualized data such as avatars in a virtual world interface orelements in a large database that can be queried, as well as othereffector elements now existing or yet to be developed.

Applications of effector elements can be for diagnostic purposes such asdetecting stimuli or body functions (e.g., visual function, visualdisease progression, mood, alertness, detecting injury such as traumaticbrain injury, cardiac electrical and/or mechanical function, subclinicalseizure detection), detecting external world situations or environmentswithout subjecting the human body to discomfort (e.g., sensing heat in afire, detecting oxygen or toxic gas content in the external environmentsuch as a mine).

Effectors can be applied for medically related therapy such as brainrelated function (e.g., brain stimulation for patients with sleepdisorders or central apnea, biofeedback for stroke rehabilitation, deepbrain stimulation for motion or seizure disorders), other neurologicaldiseases (e.g., substituting artificial sensor data in patients withperipheral neuropathy, biofeedback stimulation of muscles), cardiacdisease (e.g., arrhythmias treated with implanted devices, cardiacfunction improved with mechanical or electrical devices), response toobesity, or other organ disease modified with directed electrical ormechanical elements.

Applications of machine learned therapy using this invention can be fortraining, learning and performing of physiological activities ormechanical, non-physiologic functions. Unlike the prior art that appliesnon-specific stimulation, e.g., transcranial direct current stimulation(ref:http://www.scientificamerican.com/article/amping-up-brain-function), thepresent invention can sense, machine learn, optimize, and then deliverspecific therapy modulated via a feedback loop. This will providetailored therapy to modify many complex functions.

Other applications for this invention include improving athleticperformance after injury (e.g., direct stimulation to muscles to regainlost function, biofeedback to maintain heart rate within desired rangeduring controlled exercise, brain stimulation), enhancing sensoryperceptions (e.g., artificial visual sensors for facial recognition,artificial auditory sensors to detect previously inaudible information),performing tasks in non-typical ways by overcoming constraints ordeveloping more efficient solutions (e.g., driving a car with smallfinger movements or eye motion amplified by artificial device,controlling an external device biologically, e.g., small eye or limbmovements to control a computer interface). Examples of mechanicalfunctions include biological operation of a mechanical exoskeleton forsoldiers, performing tasks too difficult or dangerous for humans such asdeep sea exploration, armed combat, or basic tasks such as controlling acomputer, video games or remote controls.

In summary, the invention incorporates a combined biological-artificialnetwork, referred to as enciphered network (or representation), tomodulate specific tasks (such as complex bodily functions oftenrequiring brain or nerve involvement, or higher cortical functions).Sensors (biological or artificial) sense the activity of the measuredtask. This sensed activity is enciphered, and then machine learningalgorithms and specific hardware modulate the network using biological,artificial or hybrid effectors (e.g., stimulating electrodes). Thenetwork can directly augment a function (e.g., sleep), or form a newfunction via existing elements (“retasking” a function, e.g.,associating lower back stimulation with sleep).

The enciphered network can operate internally using symbolic internalrepresentation specific to each task. Specific representations of eachtask may be important because the pattern, frequency, and amplitude ofstimulation differ considerably between tasks—e.g., modulatingelectrical activity on the scalp versus the neck or other parts of thebody, or stimulating neural elements versus blood vessels.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings, in which:

FIG. 1 shows a schematic representation of the invention, includingbiological sensors or external sensors, a signal processing unit and acomputing device that uses machine learning and can interface a databaseto create a symbolic representation of bodily functions, e.g., an“enciphered functional network”. A control unit can be used to treatabnormal physiological functions via a device or biological organ(“effector”) tailored by measuring response to therapy in a feedbackloop.

FIG. 2 illustrates the relative relationship of sensors, sensedsignatures for functional domain(s), the enciphered functional network(with analysis engine), the effector group(s) for a functional domainfor a bodily function.

FIG. 3 shows a flowchart illustrating how the enciphered functionalnetwork represents a bodily function in an individual person, asfunctional domains represented by sensed signatures. Sensed signaturesare analyzed by machine learning algorithms relative to desired andundesired behavior, and to databases in the enciphered network of“population behavior” or historical behavior of that individual, tomonitor function, guide and assess response to therapy.

FIG. 4 shows an example of sensed signatures for a given bodilyfunction, for functional domains representing physiology in the nervoussystem and not in the nervous system. The portfolio of sensed signaturesbecomes the measured representation of that bodily function for anindividual person.

FIG. 5 shows examples of modifying bodily function using the encipherednetwork. Modification is tailored to the individual via personalizedsensory signatures and machine learning in the enciphered network.Modification includes therapy, such as for sleep-disordered breathing,but can also enhance normal function for that individual. Modificationoperates in a continuous feedback, assessing response via the encipherednetwork to prevent excessive or deleterious modification.

FIG. 6 shows illustrative body locations for sensed signatures andmodifying various functional domains. Sensor locations are indicated byopen (white) regions and effector (modifying) regions by filled (black)regions. Their relative size varies in each individual, is determined bymachine learning for each individual and is not portrayed to scale.

FIG. 7 shows examples of a body sensor, with a sensor element, powersource, microprocessor element, nonvolatile storage and communicationelement. Several types of sensor element are illustrated, such asphotodetector (for skin temperature, metabolic light sensing, drugsensing), galvanometer (for skin impedance), pressure (for weight, skinbreakdown), temperature or chemical. The invention can also use externalsensors (FIGS. 1, 12-18) that provide a variety of extrinsic orartificial signatures (FIGS. 12-18).

FIG. 8 shows an example of an embodiment of sensed signatures in sleepdisordered breathing.

FIG. 9 shows an example of an embodiment of effector locations for sleepdisordered breathing.

FIG. 10 shows an example of an embodiment of sensed signature for heartfailure.

FIG. 11 shows an example of an embodiment of sensed signature of bodyresponse to obesity.

FIG. 12 shows an example of an embodiment of sensed signatures for otherconditions.

FIG. 13 shows an enciphered (symbolic) network model for physiology ofsleep-disordered breathing.

FIG. 14 shows enhancement of body function using enciphered network.

FIG. 15 shows cybernetic enhancement of body function using encipheredfunctional network.

FIG. 16 shows an example of a transformation of motor function. Theflowchart shows one embodiment for enhancing motor (muscle control)function of the nervous system. This is illustrated for leg musclefunction, for enhancement (e.g., in military or sports use) or formedical purposes (e.g., after a stroke).

FIG. 17 shows an example of enhancing sensory function. The flowchartindicates embodiment for enhancing sensory perception/sensation of thenervous system. This is illustrated for alertness, for enhancement(e.g., military or sports use), for medical purposes (e.g., monitoringdrowsiness or coma) or for consumer safety (e.g., identifying drowsinesswhile driving to control a feedback device).

FIG. 18 shows an example of transformation of sensory function. Theflowchart indicates an embodiment for transposing, or enhancing sensoryperception. This is illustrated for hearing, with the inventionenhancing hearing and transposing hearing function to another nervousfunction.

FIG. 19 shows an example of creating a novel “cybernetic” sensoryfunction. The flowchart indicates an embodiment for providing a sensoryfunction that does not currently exist. This is illustrated forintegrating sensation from a biosensor for a biotoxin.

FIG. 20 shows another example of creating a novel “cybernetic” sensoryfunction. The flowchart indicates an embodiment for using the biologicalnervous system for recognition of a desired pattern.

FIG. 21 shows computer hardware for machine learning.

DETAILED DESCRIPTION

A system and method for enhancing and modifying complex functions of thebody are disclosed herein. In the following description, for thepurposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of example embodiments. Itwill be evident, however, to one skilled in the art, that an exampleembodiment may be practiced without all of the disclosed specificdetails.

The invention modulates and enhances complex and higher bodily functionsby modulating a series of functional domains. Typically, the complexfunction will include a component of brain or nervous activity. Oneinnovation is the creation of an enciphered (symbolic) representation tomodel the complex function. Such a representation model may also becalled a network, and is learned in this invention. This is created by,then used to interpret sensed signals from functional domains thatcomprise the function. The enciphered network is then used to effectchange. In one preferred embodiment, this is applied to improve sleepapnea, but other embodiments modulate heart failure, obesity, alertness,mood, memory and mental performance or cognition.

FIG. 1 illustrates an example system to modify and enhance complex bodyfunctions in a human being. Specifically, the example system 100 isconfigured to access external signals from biological sensors 104 andfrom external sensors 110.

The biological sensors 104 can sense biological signals, from anindividual, from another individual, or from a database of signals 118.The biological sensors 104 can be wearable.

External sensors 110 can sense biological signals, from an individual,from another individual or from a database of signals 118. In turn,signals may arise from the central nervous system, peripheral nervoussystem, cardiovascular system, pulmonary system, gastrointestinalsystem, genitourinary system, skin or other systems.

External sensors 110 can provide many types of information including,but not limited to, those normally sensed including pressure/physicalmovement (tactile, touch sensation), temperature (thermal information,infrared sensing), chemical (galvanic skin resistance, impedance,detection of specific ions from the skin), sound (auditory sensation),electromagnetic radiation in the visible spectrum (visual sensation),movement (a measure of muscle function and balance).

External sensors 110 can also provide information related to normalsensation but that is not normally sensed including, but not limited to,the invisible electromagnetic spectrum (such as gamma radiation, X-rays,radiowaves), sound waves outside the normal physiological range forhumans (roughly 20 Hz to 20 kHz) but including the range sensed byanimals (for instance, dogs can sense higher frequencies).

External sensors 110 can provide information outside normal sensorymodalities including, but not limited to, toxins such as carbon monoxideor excessive carbon dioxide, forms of radiation (such as alpha and betaradiation), biotoxins such as toxins of Escherichia coli bacteriaassociated with food poisoning (type 0157), anthrax or other agents.Clearly, such information would be of value for military and securityapplications.

In FIG. 1, signals are delivered either wirelessly or via connectedcommunication to a signal processing device 114 functioning with acomputing device 116 that has access to an analysis database 118. Thecomputing device 116 and signal processing device 114 communicate with acontrol device 120, which in turn controls a biological device 108 or anexternal device 112. The biological device 108 is an effector device,which can be wearable by the individual. The computing, signalprocessing and control devices with sensors and effectors together forman “enciphered functional network” (EFN).

FIG. 2 illustrates the relationship between sensors, sensed signaturesfor functional domain(s), the enciphered functional network (withanalysis engine) and the effector group(s) within the functional domainfor a bodily function. At item 150 one can see the entire functionalnetwork domain for a particular bodily function, such as sleep orbreathing. At 155 are illustrated sensors 1, 2, . . . n that are used toprovide sensed signatures 160 for this functional domain. The encipheredfunctional network 165 for this functional domain controls and analyzesthe information from the sensors and sensed signatures Of note, theenciphered network can recruit additional sensors or stored patterns(such as from a database, shown in FIG. 3) depending on its learned orprogrammed behaviors. Many forms of analysis can be performed asdiscussed below. Item 170 shows that the enciphered functional networkincludes communication with an effector group for that bodily function,which in turn signals effectors 1, 2, . . . n at step 175. A key elementof the invention is interconnectivity and links between each elementwithin/with the enciphered functional network, indicated by doublearrows.

FIG. 3 gives more detail on the enciphered functional network for anormal bodily function or abnormal bodily functions. The list of bodilyfunctions addressed by this invention are broad, and typically spanmultiple physiological systems (represented as functional domains). Theymay include but are not limited to sleep, sleep disordered breathing,cognition, mental performance, response to obesity, response to heartfailure.

In FIG. 3, a body function is represented by nervous system 220 andnon-nervous system (non-neural) 260 networks. The networks 220, 260comprise respective functional domains 230, 270, defined by signatures240, 280 based on a variety of sensors. This produces nerve andnon-nerve signatures for the body function, which can be normal 250 andabnormal 290—or desired 250 and undesired 290. It should be noted thatthe networks can interact via interactions 225 and signatures may beinter-related by relationships 245.

Machine learning algorithms of the enciphered functional network areenabled using artificial intelligence (autobot, fuzzy logic circuits).This can be done via neural networks (e.g., 3 layer back-propagationnetworks or other designs), techniques of deep learning, heuristics,linear classifiers or other forms of fuzzy logic. An important featureof such systems is that they do not need to know much about thespecifics of human pathophysiology, but need to learn information aboutfactors influencing behavior that is provided by the sensed signatures.They are thus well suited to the problem of complex bodily functionsthat are often incompletely defined or mapped pathophysiologically. Thisis not structured by theoretical “textbook” classification schemes.Certain elements of the system can be layered as rule-based, using forinstance deterministic solutions from a database such as the dermatomaldistribution of a nerve in the shoulder or the fact that somefluctuations in skin oxygenation reflect heart rate.

Thus, the symbolic model of simple and complex functions is akin torepresenting something that is visualized by an “impressionist” painterrather than a detailed physiological representation—by one trained inthe “realist” school. Again, this approach is based largely on thepremise that in addition to the primary physiological systems requiredfor a task, that is difficult to precisely define, secondary networkedregions become involved.

Machine learning nominally links signatures with normal function 250 inorder to create a patient specific range to detect abnormal function 290as outliers. In practice, the best results are obtained when the machinelearning algorithms perform repeated pattern classification interactions255 between sensed signatures for normal 250 and abnormal 290 functions.This interconnectivity is necessary, but its complexity makes the systemideally suited for a computational machine learning paradigm to modifyand treat the networks 235.

In FIG. 3, digital learned representations enable personalized diagnosisand therapy. A database of learned networks (representations) betweenindividuals forms the core of a multimodal digital network of populationhealth and disease, that is actionable—i.e., can be used to monitor andtreat disease or improve performance. For health care or screeningpurposes, this database (component 215) is de-identified, but ifindividual consent is obtained, e.g., in military or Institutionalsettings, abnormalities can be traced from or applied to specificindividuals to improve their performance in the population. This formsthe basis for a novel approach to crowd-sourced health or wellnessscreening, crowd-sourced disease monitoring, and crowd-sourced deliveryof therapy.

FIG. 4 provides detail of signatures sensed 310 by the invention torepresent a given bodily function in an individual person. Bodyfunctions comprise multiple functional domains, broadly classed asprimarily nervous system related and not nervous system relatedphysiology. Sensed nerve signatures 315 would typically represent thesensing location 320, patterns of activity 325 (e.g., periodic with acertain frequency spectrum, or more complex and potentially representednon-linearly by fractal dimension or measures of entropy), or rate offiring 330 (e.g., the fundamental or “dominant” frequency of a spectrumor first peak on an autocorrelation function).

Numerous other nerve-related parameters are possible, e.g., nuclearscans of neuro-tissue function, e.g., MIBG scanning for autonomicganglia, metabolic quantification using positron emission tomographybased sensor information, serum levels of norepinephrine and othernerve-related signatures familiar to one skilled in the art.

Non-nerve signatures 335 represent other modalities 340 that are notprimarily in the nervous system. Represented modalities have one or moredefined signatures, e.g., hypervolemia is detectable by reducedelectrical impedance of tissue, sympathetic activation via “clammyskin”—reduced galvanic skin resistance and altered ionic composition,apnea via reduced oxygenation measurable as reduced skin absorption inthe near-infrared end of the electromagnetic spectrum. These signaturesalso possess information on location 345, rate 350 and temporal patternsover time 355. Numerous other parameters are currently possible and maydevelop over time and be incorporated into this invention, e.g., tissueconcentrations of neurohormones such as B-type natriuretic peptide orprolactin from a pharmacological sensor, signal intensity from aphotodetector to detect drug concentrations in skin or cutaneous bloodvessels, drug or alcohol levels in exhaled breath from an oropharyngealsensor, drug or alcohol levels in urine from a penile sensor, and othersensors relevant to the functional domain under consideration.

The network of sensed signatures exemplified in FIG. 4 becomes themeasured representation of that bodily function for an individualperson. This is a form of “digital phenotype” of components of thebodily function. It is recognized that nervous and non-nervousphysiological elements are deeply integrated biologically, but thisformulation is a convenient approach to parameterize complex physiologyinto tracks that can be measured, mathematically modeled and learned.Other more integrated formulations are possible.

Note that not all possible measured signatures are needed for theinvention to work—in simple clinical practice, heart failure can bemonitored quite well from the simple measure of weight gain alone; thisinvention uses machine learning to mathematically weight the mostimportant signature but also to use information from whatever iscurrently available.

FIG. 5 illustrates modification of the bodily function using theenciphered network, tailored to sensed signatures. Modifications includetherapy, such as for sleep-disordered breathing, but can also includeenhancement of normal function for that individual. Modification throughthe enciphered network operates via a feedback loop, in which responseis measured to prevent excessive or deleterious modification.Nerve-related domains can be modified by direct energy delivery 400 tostimulate or suppress a domain. For instance, counterstimulation of skinon the abdominal wall (e.g., by vibration via a piezoelectric device,heat via an infrared generator) may suppress the sensation of pain fromorgans supplied by visceral nerves of lumbosacral origin (lower back).Domains 410 may thus lie in the peripheral nervous or in the centralnervous system 420, such as scalp stimulation to modify cranial nervesor light delivery to modulate the ophthalmic nerve or (indirectly)pineal gland activity. In this way, the bodily function can be treated,enhanced or otherwise altered 430. Non-nerve domains can be modified 440in many ways including vibratory stimulation via a piezoelectric deviceto stimulate a muscle, infrared heat to reduce muscle spasm to modulatethe domain 450 and network 460 to modify the bodily function 430.Notably, modification is individually tailored via personalized sensorysignatures and the enciphered network.

Modulation of nerve-related domains 410 can be linked to modulation ofnon-nervous domains by modulation connection 415. Moreover, the centraland peripheral nervous network 420 can be linked to the non-nervoussystem physiologic network by network connection 425.

FIG. 6 indicates several potential body 500 locations for sensingsignatures and modifying different functional domains. Bodily functionscan be measured by sensor 505 and/or modified by effector 510 sites.Sensor locations are shown by open (white) regions, and effector(modifying) locations by filled (black) regions. Their relative sizevaries in each individual and is not shown to scale. FIG. 6 indicatessensor locations on the body 500 to detect signatures of the nervous535, cardiovascular 540, pulmonary 540, gastrointestinal 545,genitourinary 550, skin 550 and other domains. Body functions measuredand/or modified by the enciphered functional network include sleep andcentral sleep apnea 515, cognitive performance 520 such as alertness,obstructive sleep apnea 525, and the bodily response to obesity 530. Avariety of signatures are indicated by way of example and not to limitthe scope of the invention. These are discussed in more detail withregards to other figures in this disclosure.

FIG. 7 illustrates an example of a body sensor 600, comprising sensorelement 605, power source 610, processing components 615, nonvolatilestorage 620 (e.g., E2PROM, powered RAM), communication element 625 on astructural platform 630. Several types of sensor elements areillustrated Biological sensors include, but are not limited to,photosensitive sensors 640 to detect skin reflectance (indicatingoxygenated hemoglobin, and perfusion), galvanometers 650 to detect skinimpedance or conductance (a measure of body chemistry), transcutaneousor invasive nerve activity (neural electrical activity) or muscleelectrical activity (myopotentials), pressure detectors 660 (to detectpressure, e.g., weight, mechanical joint movement or position), thermaldetectors 670 to detect temperature (a measure of metabolic activity andother disease states), and chemical detectors 680 to perform assays fornorepinephrine or drugs, body pH from the skin, mouth, or elsewhere inthe gastro-intestinal or genitourinary tracts, enzymatic profile in thegastrointestinal tract, DNA profile (for instance, a gene chip on thelining of the mouth), and other sensors such as for heart rate,ventilation (breathing).

The invention can also use external sensors (FIGS. 1, 12-18) thatprovide a variety of extrinsic or artificial signatures (FIGS. 12-18) toprovide cybernetic sensor inputs or effectors to the encipheredfunctional network.

FIG. 8 indicates an example embodiment of sensed signatures insleep-breathing disorders. As typical for many bodily functions,sleep-disordered breathing impacts multiple nervous and non-nervoussystem domains. While all domains can be sensed, not all domains need tobe sensed in every patient, and the actual sensed domains (and hencesensors) can be tailored to signatures in a given individual andpractical considerations. As seen in FIG. 8, sensor types can includebut are not limited to skin impedance, other electrical sensors (nervefiring in the periphery and on the scalp, and heart rate), temperature,chemical sensors, optical sensors of skin color (that can detect oxygensaturation of peripheral blood), motion sensors and pressure sensors.

FIG. 9 indicates example embodiments for various effector or treatmentoptions for sleep-disordered breathing using the enciphered functionalnetwork. These are provided by way of example and in no way limit thescope of effectors and treatment options that can be provided for thiscondition or other bodily functions. The body 800 is interfaced witheffector devices 810, tailored to each modality. For sleep apnea 820 ofthe central type, examples include direct stimulation of breathingcenters including the brain (via low energy scalp stimulation),accessory muscles in the neck and the diaphragm. For obstructive sleepapnea, examples include direct stimulation of pharyngeal and neckmuscles to maintain tone and prevent obstruction. For central sleepapnea, the invention can activate pro-breathing centers, tricking thebrain to breathe more by stimulating sensors of low oxygenation/highcarboxyhemoglobin in the finger, by providing CO2 or equivalent index oflow breathing to regions of the periphery that are not harmful. Forcentral and obstructive forms of sleep apnea, there is evidence thatchest edema accumulates and can be measured as increasedrostral-to-peripheral ratio of skin impedance (FIG. 7). Accordingly,controlled negative pressure in the lower extremities 840 can reversethis rostral fluid accumulation. Direct stimulation of pro-sleep centersby other methods 850 include stimulating the pineal gland through lightexposure of the appropriate wavelength in the visible and infraredspectra. Light can be provided in patterns that are specific to eachindividual. Other pro-sleep sensors include activation of vibratorysensors 860 to mimic the somnorific impact of massage, or stimulation ofpost-prandial satiety sensors 870 including stimulating peripheral skinsensors of hyperglycemia. Other specific stimuli can also be provided asfamiliar to one skilled in the art of sleep disorders, and can be addedto the infrastructure of the invention as new modalities and sensedsignatures are developed.

FIG. 10 indicates an example embodiment of sensed signatures in responseto heart failure. As typical for many bodily functions, heart failureimpacts multiple nervous and non-nervous system domains. While alldomains can be sensed, not all domains need to be sensed in everypatient, and the actual sensed domains (and hence sensors) can betailored to signatures in a given individual and practicalconsiderations. As seen in FIG. 10, sensor types can include but are notlimited to skin impedance, other electrical sensors (nerve firing in theperiphery and on the scalp, and heart rate), temperature, chemicalsensors, optical sensors of skin color (that can detect oxygensaturation of peripheral blood), motion sensors and pressure sensors.

FIG. 11 indicates an example embodiment of sensed signatures in responseto obesity. As typical for many bodily functions, obesity impactsmultiple nervous and non-nervous system domains. While all domains canbe sensed, not all domains need to be sensed in every patient, and theactual sensed domains (and hence sensors) can be tailored to signaturesin a given individual and practical considerations. As seen in FIG. 11,sensor types can include but are not limited to skin impedance, otherelectrical sensors (nerve firing in the periphery and on the scalp, andheart rate), temperature, chemical sensors, optical sensors of skincolor (that can detect oxygen saturation of peripheral blood), motionsensors and pressure sensors.

FIG. 12 shows an example of sensed signatures for other conditions. Oneexample is for chronic obstructive pulmonary disease which, as typicalfor many bodily functions, impacts multiple nervous and non-nervoussystem domains. While all domains can be sensed, not all domains need tobe sensed in every individual. The actual sensed domains (and hencesensors) can be tailored to signatures in a given individual andpractical considerations. As seen in FIG. 12, sensor types can includebut are not limited to skin impedance, other electrical sensors (nervefiring in the periphery and on the scalp, and heart rate), temperature,chemical sensors, optical sensors of skin color (that can detect oxygensaturation of peripheral blood), motion sensors and pressure sensors.

FIG. 13 summarizes the invention, a computerized representation of acomplex body function, paired to biological and artificial (cybernetic)sensors, and biological and artificial (cybernetic) effectors. Theenciphered functional network is trained by machine learning algorithmsfor specific bodily functions. In the simplest case, sensed and effectorfunctions are natural physiological functions, such as sensing a painfulstimulus from the leg and moving the leg away. In more complexembodiments, the invention has the ability to enhance normal function(performance enhancement), enhance impaired function (e.g.,sleep-disordered breathing) or treat a disease or in cases where normalfunction cannot be manifest (e.g., in warfare or other situations ofconstraint).

More specifically, FIG. 13 outlines an enciphered network forsleep-disordered breathing. The left panel shows the actual physiologymeasured for sleep disordered breathing, while the right panel shows thecomputerized representation of the enciphered functional network.

In measuring the actual physiology of sleep-disordered breathing in anindividual 1200, biological signals are sensed 1205. These includebiological signals of control regions 1210 including activation of theamydala and other parts of the limbic system that control alertness,wakefulness and relate to sleep. These signals have scalprepresentations that can be detected by skin nerve sensors, but can alsobe detected by medical devices such as the BOLD signal from functionalmagnetic resonance imaging, or metabolic images from positron emissiontomography in medical applications. Physiologically, sleep is alsotriggered from intrinsic but natural signals such as darkness, sound(e.g., soothing music or the sound of waves), tactile sense (e.g.,massage of parts of the body). The intrinsic sleep control regions ofthe brain 1210 then integrate these inputs with sensors related tobreathing including low oxygenation, measureable in the fingertips 1225,that stimulates breathing, and stimulation of the diaphragm 1220 toenable ventilation of the lungs.

The schematic shown in the left panel of FIG. 13 is of course asimplified view of sleep-related-breathing, but it illustrates how aseries of sensors and effectors are integrated by the biological controlregions. Other sensors and effectors can be involved at other times, andcan be measured in connection with the sleep-related breathing. This isa strength of the invention, that additional sensed signals can be addedand will be adaptively integrated by the enciphered network.

In the right panel of FIG. 13, the parallel enciphered network forsleep-disordered breathing also has sensors, control logic andeffectors, but these are a combination of biological and engineered(artificial) components. Sensors can detect intrinsic signals 1240 (suchas oxygen saturation) or extrinsic signals 1245 (such as the presence,intensity and patterns of visible light). A sensor matrix 1250 thencombines these biological and non-biological signals either separatelyor by multiplexing them, e.g., using a weighted function. Thecomputational logic 1255 is the central processor of the encipheredfunctional network.

The computational element 1255 forms a symbolic relationship betweensensed signals and biological function (e.g., elements 250-290 in FIG.1). It is linked to a database 1260 to store multiple states for thisindividual person as training datasets for machine learning (i.e., fuzzylogic, artificial intelligence) in order to learn normal sleep patternsand breathing from disordered ones (elements 250 versus 290 in FIG. 2).This is then mapped to effectors 1265 that can be biological, such asbrain regions (related to control regions 1210 and unrelated to controlregions 1210) as well as muscles (the diaphragm 1220 as well as othermuscles that are less notable but also involved in sleep such as thelevator labii superioris alaeque nasi muscles). Effectors can also becybernetic 1275, in that they interface artificially engineered deviceswith the body. For instance, a peripheral low oxygen state can bemimicked by small wearable chambers (“treatment gloves”) surrounding oneor more fingers that will stimulate breathing from intrinsic sleep-braincontrol centers (control regions 1210). Similarly, appropriate learnedpatterns of light or of vibratory stimuli can be applied usingappropriate devices, to stimulate sleep-breathing patterns learned fromnormal states and stored on the database 1260.

The symbolic relationship of the enciphered network in FIG. 13 is amathematical relationship. This relationship is empirical and functionalthat uses machine learned relationships between sensed signatures andbody function in each individual—and not on detailed neurophysiologicalmapping. It is thus distinct and may not be concordant with “classical”neurophysiology. For instance, sufficient pain in the leg causeselevated nerve activity in other parts of the body. This will produce“associated with leg pain” signals in sensors located more convenientlyin the body. The empirical functional relationship is mathematical, andcan be deterministic (e.g., equation based), or can be trained/learnedsuch as via neural network.

In the simplest case, the enciphered symbolic relationship is a matrixin which a signal X causes a function Y; for instance, a noxiousstimulus such as pain sensed by a sensor/sensory nerve in the leg (X)causes activity in a motor nerve causing withdrawal of that leg (Y).This function is not represented in the device based upon a detailedneurophysiological representation of leg sensation (in the primarysomatosensory cortex, in the post-central gyrus), or the precise nervesthat control the leg. Instead, this function is mappedempirically—sensation on any nerve associated with the painful stimuluscan result in actions leading to leg withdrawal.

The advantage of this approach is that it can analyze the multipleeffects of a particular stimulus. For instance, an acute painfulstimulus often produces activation on nerves remote from the originalsite of stimulation. Hence, pain in the leg, that may be inaccessible,may be detected from nerve activity quite distant from the sensationsuch as the chest wall, that may be more accessible.

In FIG. 13, generalizing from the example for sleep-breathing, sensingis processed and results in output to an effector. For instance, thesensed noxious stimulus can produce an effector function to move theleg, or control a device to administer a pain killing medication ortherapy. In other examples that will be discussed below, the stimuluscan move a prosthetic limb or alter biological function.

Moreover, FIG. 13 shows that the enciphered network determines preciseaction by defining interactions with the device or bodily function. Thisis a programmed function, depending upon the desired functionality ofthe invention. This then produces a real output requiring application ofenergy that results in interaction with the device or a bodily function.

FIG. 14 illustrates a preferred mode of action of the invention toprovide computational enhancement of the bodily function via theenciphered functional network. The flowchart for the invention sensessignatures for a given bodily function 1305, comprising biologicalsignals (e.g., breathing rate, finger oxygenation) or extrinsic signals(e.g., tissue impedance indicating volume load, emitted infraredindicating temperature, or carbon dioxide concentrations in exhaled airindicating the efficiency of breathing).

Item 1310 applies the symbolic model of the enciphered network, asidentified in FIG. 8 to map sensed signals to a bodily function based onpractical measurable signatures rather than classical, detailedphysiology mapping that may be ill-defined, rapidly changing andinaccessible to measurement.

As described above, the symbolic model uses machine learning to mapsensor input to normal and abnormal function of that bodilyfunctionality. This comprises training sets of different patterns forthat individual, that are both personalized and continuously adaptive.

In FIG. 14, step 1315 transforms an effector (motor) function, i.e.,controlled by an existing motor nerve. In step 1320, the motor nervesignal is “re-routed” to control a prosthetic device or another musclegroup. For instance, in the case of an amputee, the signature of motornerve output to the leg may be detected from the skin above theamputation site. The range of sensed nerve activity on the skin maytypically be 7-15 Hz (depending on the precise nerve). Sensing thesesignals, and mapping them to specific movement of a prosthetic limb mayenable control of the limb. This control may require subsequenttraining—for instance, behavioral training in which the individualattempts to flex the amputated limb, and detecting the skin signals asthose that will flex the prosthetic limb in that person. Similarpersonalized mapping is used to train other motions of the prosthesis.In this instance, the invention is one embodiment of a personalized“enciphered nervous system”.

In FIG. 14, step 1325 is another embodiment—to enhance performance ofthis body function. Instead of expending the energy required to move afinger, the enciphered network can sense sub-threshold activity of themotor nerve and “boost” the signal to move the finger 1325. This isuseful for individuals with nerve degeneration, those withmusculoskeletal disorders or those under some form of sedation who wouldnormally not be able to communicate via this finger.

Furthermore, the invention can 1325 artificially generate signals neededto stimulate the muscle. Since the frequency and amplitude of nerveactivity that controls a muscle lies within a range for each individual,the enciphered network can simulate the nerve activity controlling thequadriceps femoris muscle and deliver it programmatically to regions ofthe skin associated with contraction and relaxation of that muscle forthat individual (part of the functional domain). This can be used whenthe nerve is degenerated or anesthetized (for instance, to preventpressure ulcers in patients on prolonged ventilation). It can also beused for performance enhancement—for instance, to perform isometricexercises during rest or sleep to prevent or reverse muscle atrophy, orto improve muscle function or increase metabolic rate to lose weight.

In FIG. 14, step 1330 is another embodiment of the invention—to retaskbiological motor activity. In this case, it is directed to control anartificial device. This cybernetic application is further developed inFIG. 14. In FIG. 13, instead of actually moving a finger to control aremote control unit for an electronic device, nerve activity below thethreshold of actually moving that finger will control the device. Thisenables functionality without expending as much biological energy, andalso in individuals who have lost biological function or are constrainedand unable to perform that motor function (e.g., in a militarysituation). Sensors on the finger detect this subthreshold motor nerveactivity (e.g., of lower amplitude than biologically required to movethe finger), and the enciphered network converts this to signals thatrepresent play, pause, rewind or other functions and transmits them tocontrol the remote control unit. This may be for a consumer device.Clearly, this function can be extended to training an individual to movea portion of the face to represent the “play” function, and having asensor transduce this function, and similarly for other surrogateregions of the body and retasked functions.

In FIG. 14, step 1335 is a distinct embodiment that transforms sensedsignals. Step 1340 retasks the sensed signal. For instance, sensation ofa specific smell that is trained over time, can elicit a differentresponse or control a device. Step 1345 improves performance, augmentingbiological outside of normally sensed ranges. For instance, sensingsignals in the “inaudible to humans” frequency range, transducing thesignal to the audible range, and transmitting it via vibration (bonyconduction) to auditory regions of the brain (auditory cortex) could beused for private communication, encryption, recreation or otherpurposes. Medically, this invention could be used to treat hearing loss.This same invention with sensors of vibration could be used tocompensate for loss of this sensation in diseases such as peripheralneuropathy, by transmitting this sensation to an intact sensation in anearby or remote part of the body.

Another embodiment of performance improvement (step 1345) is to increasealertness. Stimulation of the scalp in the temporal region and otherfunction-specific zones can increase brain activity in these regions.The invention tailors stimulation to the enciphered representation ofawakeness (i.e., alertness). As a corollary, drowsiness can be detectedby the enciphered network and used in a feedback loop to trigger lowintensity stimulation by a cutaneous device elsewhere on the body. Thishas several applications, including detecting and trying to preventdrowsiness while driving, in the intensive care unit during pre-comatosestates or during drug-overdoses, as a monitor for excessive alcohol ormedication ingestion, or during excessive fatigue states, e.g., in themilitary.

Sensors can detect alertness versus drowsiness from large groups ofneurons using electroencephalography (EEG) over a wide range offrequencies. EEG signals have a broad spectral content but exhibitspecific oscillatory frequencies. The alpha activity band (8-13 Hz) canbe detected from the occipital lobe (or from electrodes placed over theoccipital region of the scalp) during relaxed wakefulness and increasewhen the eyes close. The delta band is 1-4 Hz, theta from 4-8 Hz, betafrom 13-30 Hz and gamma from 30-70 Hz. Faster EEG frequencies are linkedto thought (cognitive processing) and alertness, and EEG signals slowduring sleep and during drowsiness states such as coma and intoxication.

In FIG. 14, step 1350 is a function detecting and/or forming a de novofunction. One example is creating a cybernetic “sixth sense”—that is,adding to the 5 biological senses using artificial sensors to detect anextended set of stimuli. The set of sensors is nearly infinite, butincludes several of particular relevance to the field of industrial ormilitary use, including sensors for alpha or beta-radiation. Oncesensed, the enciphered network can transduce this signal to an existingsense, such as vibration delivered through a skin patch to a relativelyunused skin region, e.g., lower back. A combat soldier exposed to alphaor beta particles will now “feel” radiation as a programmable/trainableset of vibrations in his lower back. Similarly, sensors for carbonmonoxide or other respiratory hazards could be transduced as “sixthsenses” into—for instance—low frequency vibration on the nostril. Thisapproach is far more efficient than a visual readout or other existingdevices—because they use the enciphered network to essentially reprogramthe natural nervous system for these functions.

FIG. 15 generalizes cybernetic enhancement of body function using theenciphered network. This is a further application beyond the use ofintrinsic biological signals. One application is to apply purposefulinterventions when natural body functions are constrained, e.g., asoldier can use a finger to activate a device if his/her foot cannotactivate a pedal due to an obstacle, or, in an amputee, interfacing arobotic arm to specific nerve fibers that formerly controlled thebiological arm.

FIG. 15 is an embodiment in which intrinsic biological signals andextrinsic non-biological signals are sensed (step 1400). The encipherednetwork does not simply map learned function to sensed signals, butinstead extrapolates from learned functions to create novel function1410. The enciphered representation of the body function to sensedsignals is extended to a personalized network in step 1420 via machinelearning. This involves a series of steps, including 1430 multiplexingor otherwise combining intrinsic with extrinsic signals, toprogrammatically modify external signals in a personalized fashion.Signal multiplexing is performed to achieve the desired function 1440that may be storage of non biological information (e.g., word processingdocuments, images) in the patient's brain, i.e., using biologicalstorage as digital memory, and so on. Signals can be combined based ondata from this person alone, from a database of multiple individuals(e.g., item 1260 in FIG. 12), or by a technique such as crowd-sourcingin which information from multiple persons is integrated to train theenciphered network. Data from multiple persons could be combined in aformal database, or by applying machine learning to the wider set ofsensed signals and biological outputs between individuals (not just forone individual).

Step 1450 in FIG. 15 shows the effector layer, the interface between theoutput of the enciphered network for a designed cybernetic function anda series of biological (e.g., motor nerve, muscle) or external (e.g.,prosthetic limb, computer) effector devices.

Several embodiments exist. In step 1460, the invention uses a biologicalsignal to control an external device (e.g., motor nerve control of aprosthetic limb), or an external signal to control a biological function(e.g., external signal stimulation of a skeletal muscle). As described,skeletal muscle is typically stimulated by nerve activity at a frequencyof 7-15 Hz (varying with precise nerve distribution, see Dorfman et al.Electroencephalography and Clinical Neurophysiology, 1989; 73: 215-224).Such external stimulation can improve muscle strength by stimulating it,and would enable performance improvement of, e.g., programmableimprovement in leg muscle function. Another example is to treat centralsleep apnea, using an external sensor of oxygen desaturation (“desat”)to activate a device that stimulates the phrenic nerve and hence thediaphragm. This may have substantial clinical implications.

FIG. 15 step 1470 shows an embodiment in which the invention replaces abiologically lost or unavailable function in that individual withfunction from the enciphered network. This is an extension of boostingperformance in FIG. 14 (step 1325). For instance, the unavailablefunction of hearing outside the normal 20 Hz to 20 KHz range can beprovided using external sensors and the signal transduced to the audiblefrequency range (e.g., vibrations delivered via bone conduction to theinner ear using a device placed near the mastoid processes, e.g.,attached to the side-arms of eyeglasses) or to another sensible modality(e.g., vibration on the arm). In an individual with hearing loss, thesensed signal will lie within the normal but compromised auditory rangefor this individual.

In FIG. 15 step 1480, the invention enables biological control of acomputer. An example of this function is to provide an intelligentcontrol framework for an infusion pump. For instance, glucose control isnot determined simply by the reaction of the pancreas and other sensingregions to plasma glucose. Instead, higher brain centers that controlactivities of daily living anticipate actions such as imminent exerciseor stress, and produce increased heart rate and a hormonal surge (e.g.,adrenaline, epinephrine) that in turn increases blood glucose. Currentglucose infusion pumps actually cannot mimic such higher cognitiveinput, and instead wait for drops in glucose from metabolic demandsbefore infusing glucose. Such devices will always lag behind idealphysiological control and will produce suboptimal performance.

In FIG. 15 step 1490, the invention can provide de novo functionality.This exploits the full potential of the enciphered functional network,in this case for the nervous system, and extends beyond sensory or motorperformance improvement in steps 1325 (motor) or 1345 (sensory).

In FIG. 15 step 1490, novel functionality can be provided for motorfunction (i.e., previously unavailable movements) or sensory function(i.e., a cybernetic 6th sense). A large proportion of cerebralprocessing power is dormant at any given time, but may be activatedsubconsciously during daily activity (e.g., daydreaming). The encipherednetwork can access some of this brain capacity to use the biologicalnervous system as a computer. One task for which the human brain/nervoussystem is particularly adept is pattern recognition. Recognition offaces, spatial patterns and other complex datasets is performed bypeople far better than by artificial computers. The selected exampletrains the individual to detect the pattern via repeated overt orsubclinical exposure to an image. The biological response to this image(symbolic representation) is detected by sensors on the temporal orfrontal scalp. Again, this is empirical—the primary memory encodingregions do not have to be identified or mapped, and it is sufficient tosense a secondarily activated region of the brain/scalp. Once this isaccomplished, detection of the pattern or a similar pattern willsubconsciously trigger the response that can be sensed and coded as a“1” or “0” to control a device (e.g., a pattern classifier computer) orcause a certain function—such as to trigger an alarm if this is adangerous pattern/image.

FIG. 16 illustrates an embodiment of motor function controlled by theenciphered network. The Flowchart in FIG. 16 provides a preferredembodiment to transform leg movement. A symbolic model is to link motornerve function, sensed at a signature of the primary motor region(scalp, near the superior portion of the contralateral precentral gyrus)or a secondary region, with a plurality of leg motions in step 1510.Once done, functional mapping can be reprogrammed using external sensedsignals (step 1515) including those not normally associated with legfunction. An example would be for motion in an index finger to controlthe leg movement, in patients with leg disease or soldiers who cannotmove their leg in a certain task. Functional mapping can also use theexisting signal (step 1520).

In step 1525, a signal multiplexor links the intrinsic or extrinsicsignals in order to control the desired programmed function. In step1530, this is achieved to enhance biological leg function (e.g., viacutaneous/direct electrical stimulation as described). In step 1535,this is performed to control a prosthetic limb.

FIG. 17 shows an embodiment of enhancing sensory function via theenciphered network. FIG. 17 is an embodiment for enhancing alertness. Asymbolic model is created in step 1610 using a signature of sensed scalpnerve activity, e.g., from the temporal region that is empiricallyassociated with alertness. Functional mapping is reprogrammed usingintrinsic sensed signatures (step 1615) or signals not normallyassociated with alertness (e.g., a specific auditory sensed frequency),or the existing scalp signal (step 1620). In step 1625, a multiplexorlinks the intrinsic and extrinsic signals with an effector to achievethe desired function—electrical stimulation of the scalp to increasealertness (step 1630). Step 1635 provides an alertness monitor to alarmor produce the desired function, and that can detect and try to avoiddrowsiness or coma, such as during driving, on the battlefield or fromtoxin ingestion.

FIG. 18 depicts an embodiment of the invention to transform sensoryfunction. FIG. 18 is a flowchart of an embodiment to enhance sensoryperformance—in this case hearing. Step 1710 is the symbolicrepresentation of sensed signals from a readily accessible sensor of thesignature near the ear, as well as secondarily associated skin regions.Step 1715 uses sensors to detect signatures of frequencies outside thenormally sensed frequency spectrum. Step 1720 uses a signal normallyassociated with hearing. Step 1725 uses a multiplexor and control logicto transduce the signal to the audible range (step 1730), transmittedvia vibration (bony conduction) to the hearing regions of the brain(cochlear nerve/auditory cortex) using a device that could be used forprivate communication, encryption, recreational or other purposes.Medically, this invention has application as a sophisticated hearingaid. This same invention with vibration sensors compensates for loss ofthis sensation in diseases such as peripheral neuropathy, bytransmitting this sensation to an intact sensation in a different partof the body. At 1735, the multiplexor transduces this signal to adifferent “surrogate” sensation, e.g., skin stimulation.

FIG. 19 shows an embodiment to create novel “cybernetic” sensoryfunctions. FIG. 19 is a flowchart of an embodiment to create acybernetic “sixth sense” (e.g., sensing a biotoxin). The inventionsummarized in FIG. 19 incorporates information associated with theexample of sensing carbon monoxide. Specific sensed signals causedamage, to calibrate sensing and delivery of therapy functions. Forinstance, exposure to carbon monoxide is dangerous, yet this toxin isoften undetected. Federal agencies in the U.S. such as OSHA put ahighest limit on long-term workplace exposure levels of 50 ppm, with a“ceiling” of 100 ppm. Exposures of 800 ppm (0.08%) lead to dizziness,nausea, and convulsions within 45 min, with the individual becominginsensible within 2 hours. Clearly, an invention to detect this toxinearly and cause biofeedback through the enciphered nervous system mayhave extremely practical implications in industrial environments. Othernomograms can be developed to identify thresholds for “safe” versus“actionable” exposure to various stimuli including but not limited tochemicals, biological toxins, radiation, electrical stimuli, visualstimuli and auditory stimuli.

The invention summarized in FIG. 19 can also be used to create novelhuman functionality, by using the enciphered network to pair sensedbiological or external signals to any programmed biological or externaldevice. It thus forms an embodiment of a cybernetic nervous systemoperating in parallel with the body's natural nervous system. The extentto which these nervous systems are parallel or integrated will dependupon the extent to which sensed signals are multiplexed and effector“control” signals are combined. Examples are discussed below.

The invention outlined in FIG. 19 thus provides hitherto unavailableprogrammatic control of plasticity—that is, actually observed at somelevel on a regular basis in normal life. In the realm of sensoryphysiology, training can enable an individual to perceive a sensationthat was previously present but not registered/recognized. Examplesinclude musical training to detect tonality, or combat training todetect subtle sounds or visual cues. In the realm of motor control,physical training can enable an individual to use muscle groups thatwere previously unused. In the realm of disease, normal “healingfunctions” cause undiseased regions of the central nervous system totake over functions now lost due to a stroke (cortical plasticity), orunaffected peripheral nerves to take over functions of a nerve lost dueto trauma or neuropathy (expansion/plasticity of peripheral dermatomes).

The current invention extends known interventions based upon corticalplasticity. For instance, it is known that the dermatomal distributionof a functioning peripheral nerve expands when an adjacent distributionis served by a diseased nerve. In other words, the same function can nowbe served by different regions of the central or peripheral nervoussystem.

The invention also substantially extends normal plasticity—byprogramming desired and directed regions of the body to sense and effectfunctions normally reserved for other regions of the body that arecurrently inaccessible (e.g., in military combat) or unavailable (e.g.,due to disease).

The invention also substantially advances normal plasticity byintegrating external sensors (e.g., for normally inaudible soundfrequencies or sensations) or devices (e.g., prosthetic limbs, otherelectronic devices) into the ENS.

FIG. 19 may also include embodiments for enhancing sensory alertness.The steps are analogous to the prior examples. The symbolic model ofscalp sensed nerve activity, e.g., in the temporal region is empiricallyassociated with varying alertness levels (self-reported or monitored) instep 1710. This functional mapping is reprogrammed using external sensedsignals (step 1715) or signals not normally associated with alertness(e.g., a specific auditory sensed frequency), or the existing scalpsignal (step 1720). In step 1725 a signal multiplexor mathematicallyassociates the non-associated or associated signals to program thedesired function—electrical stimulation of the scalp to increasealertness (step 1730). Step 1735 provides an alertness monitor that canprovide an alarm or actually result in stimulated function (to close theartificial/cybernetic feedback loop in the enciphered nervous system) todetect and try to avoid drowsiness, coma or toxin ingestion.

FIG. 19 depicts an embodiment to use the ENS to integrate functionalitythat does not exist in nature into a personalized biofeedback loop—inthis case, detecting a toxin. Examples include inhalation of carbonmonoxide, a toxic gas that is colorless, odorless, tasteless, andinitially non-irritating, that is very difficult for people to detect.Another example is exposure to a biotoxin, that may not be sensed untilsymptoms and signs of a disease occur hours, days weeks later. Theinventive approach to provide a “sixth sense” (step 1800) is cybernetic,since the toxin may produce both a direct signal from a specific sensor(detected at step 1820) and an associated biological signal (step 1830),that are blended (or multiplexed) in the invention. Examples of a directsignal from a dedicated sensor (element 1810) are the chemical detectionof carbon monoxide, or a biological assay for an infective agent(viruses, bacteria, fungi). Ideally, this sensor operates in near-realtime, although this is not a requirement and if not the case will simplyprovide a slower, non-real time signal. Examples of an associatedbiological signal to carbon monoxide—a toxin that is traditionallyconsidered “unsensed”—is the specific cherry red colorimetric change ofhemoglobin from carbon monoxide and the non-specific reduction inoxygenated hemoglobin that results when carbon monoxide binds to oxygenbinding sites.

FIG. 19 further depicts that the enciphered nervous system of theinvention forms an associative symbolic representation (step 1820)between the direct and associated biological sensed signals. Thesymbolic relationship may include a direct mathematical transform, suchas a quantitive relationship of the sensed signal to carbon monoxide orthe associated biological signal of cherry red discoloration ofhemoglobin to biologically relevant concentrations. The symbolicrelationship may also use an artificial neural network or otherpattern-learning or relational approaches to link, e.g., elevated heartrate or oxygen desaturation to the toxin.

In FIG. 19 step 1840, signals are multiplexed in a non-linear analyticalfashion, as defined in the symbolic representation for any specifictoxin. Computer logic is then used to control a biological or artificialeffector device. Several therapy or monitor functions can be programmedto close a biofeedback loop. For instance, the signal from the normallyunsensed toxin can be transduced into a specific signal on a naturallysensed “channel” (step 1860), e.g., low intensity vibration on skin onthe nostril (intuitively linked with inhalation), or stimulation of skinover a scalp region normally associated with deoxygenation. This latterbiofeedback uses information from training related to the individualperson (contributing to the personalized enciphered nervous system), ora database of symbolic representations from many individuals associatingrelated stimuli (here, de-oxygenation) to biological signals. This is anexample of a population-based, or potentially crowd-sourced encipherednervous system. Another biofeedback option is therapeutic(1860)—delivery of an antidote, by sending control signals to a device.For carbon monoxide exposure, therapy includes increasing oxygenconcentrations (using hyperbaric oxygen in extreme cases) andadministering methylene blue.

Nomograms of the detrimental impact of sensed signals are used tocalibrate sensing and delivery of therapy functions from the encipherednervous system. For carbon monoxide, exposures at 100 ppm (0.01%) orgreater can be dangerous to human health. Accordingly, in the UnitedStates, Federal agencies such as OSHA put a highest limit on long-termworkplace exposure levels of 50 ppm, but individuals should not beexposed to an upper limit (“ceiling”) of 100 ppm. Exposures of 800 ppm(0.08%) lead to dizziness, nausea, and convulsions within 45 min, withthe individual becoming insensible within 2 hours. Clearly, detectingthis toxin early would have extremely practical implications inindustrial environments, for instance. Other nomograms can be developedto identify thresholds for “safe” versus “actionable” exposure tovarious stimuli including but not limited to chemicals, biologicaltoxins, radiation, electrical stimuli, visual stimuli and auditorystimuli.

FIG. 20 provides another embodiment using the enciphered network toaccess to the processing power of the natural nervous system to performan arbitrary task, such as pattern recognition (step 1905). Thisembodiment of the invention is based upon 3 concepts. First, that thebrain is more efficient at some tasks than even the most powerful andwell-programmed artificial electronic computers. Pattern recognition,e.g., facial recognition, is an excellent example that is easilyaccomplished by most people yet that is suboptimal by computers evenwith very sophisticated programming. Second, that the brain output froma presented stimulation can be sensed. Third, that the brain has unusedcapacity that can be accessed for this purpose. For instance, for neuralprocessing, only a minority is used even in highly stressful humanactivities such as warrior combat (e.g., 40% capacity used). In highlyfocused, non-life-or-death situations, a minority is still used, likely20-40%, e.g., NBA finals, SAT testing. Therefore, there is substantialresidual capacity at any one time. This third item also presents safetylimits, however, and in the case of pattern recognition, the inventionmust not be used for bioencoding images or data that would beemotionally harmful or sensitive.

Steps 1910 and 1915 link the pattern (e.g., a face) to the biologicalsensed response—for instance, activity of nerves in the scalp over theparietal lobes of the brain, or over the forehead indicating“recognition”. This is used to create the elements of enciphered nervoussystem for this task (step 1920). This will be personalized, but canalso take inputs from a multi-person (population, crowd-sourced)encyphered nervous system. Once this link has been made, thenpresentation of the pattern will result in a “sensed” biologicalpattern, which is used by the multiplexer or control logic in step 1925to deliver a “1” (recognized) or “0” (not recognized) to control adevice (step 1930) (e.g., external computer classifier) or stimulate theindividual via a surrogate sensation (step 1935) (e.g., vibration at theleft upper arm if a recognized pattern is detected). Uses for thisinvention include pure biocomputing (pattern recognition of familiar orabstract shapes/codes), formally encoding and enhancing memory of facesfor a particular person, and security such that only a hostilepattern/face elicits a specific surrogate sensation or activates adevice. One other advantage of this approach over waiting for acognitive recognition of the pattern is that this can function as a“background process” and/or provide faster pattern recognition.

Thus, this invention can improve and enhance function of traditionalsenses, if a device is used that integrates sensors that sense outsidethe normal physiological range can be used to enhance the range ofnormal physiological sensation. For instance, sensing signals in the“inaudible to humans” part of the frequency spectrum, transducing thesignal to the audible range, and transmitting it via bony conductionusing a device could be used for private communication, encryption,recreational or other purposes. Medically, this invention could be usedto compensate for hearing loss. This same invention with sensors ofvibration could be used to compensate for loss of this sensation incertain neurological diseases such as peripheral neuropathy, bytransmitting this sensation to an intact sensation in a different partof the body.

Important safety issues must be raised at this stage. While no untoward,dangerous or otherwise undesired functionality has been observed withthis invention, certain limits must be imposed. First, no stimulationintensity provided by the device can reach painful or dangerous levels.Second, no sensory input can be allowed to reach disturbing or undesiredlevels. Third, any sensor or device (effector) should have acceptableand tested safety profiles.

FIG. 21 is a block diagram of an illustrative embodiment of a generalcomputer system 2000. The computer system 2000 can be the signalprocessing device 114 and the computing device 116 of FIG. 1. Thecomputer system 2000 can include a set of instructions that can beexecuted to cause the computer system 2000 to perform any one or more ofthe methods or computer based functions disclosed herein. The computersystem 2000, or any portion thereof, may operate as a standalone deviceor may be connected, e.g., using a network or other connection, to othercomputer systems or peripheral devices. For example, the computer system2000 may be operatively connected to signal processing device 114,analysis database 118, and control device 120.

In operation as described in FIGS. 1-20, the modification or enhancementof the nervous system of the body by creating and using an encipheredfunctional network as described herein can be used to enhanceperformance in normal individuals or restore or treat lost function inpatients.

The computer system 2000 may be implemented as or incorporated intovarious devices, such as a personal computer (PC), a tablet PC, apersonal digital assistant (PDA), a mobile device, a palmtop computer, alaptop computer, a desktop computer, a communications device, a controlsystem, a web appliance, or any other machine capable of executing a setof instructions (sequentially or otherwise) that specify actions to betaken by that machine. Further, while a single computer system 2000 isillustrated, the term “system” shall also be taken to include anycollection of systems or sub-systems that individually or jointlyexecute a set, or multiple sets, of instructions to perform one or morecomputer functions.

As illustrated in FIG. 21, the computer system 2000 may include aprocessor 2002, e.g., a central processing unit (CPU), agraphics-processing unit (GPU), or both. Moreover, the computer system2000 may include a main memory 2004 and a static memory 2006 that cancommunicate with each other via a bus 2026. As shown, the computersystem 2000 may further include a video display unit 2010, such as aliquid crystal display (LCD), a light emitting diode such as an organiclight emitting diode (OLED), a flat panel display, a solid statedisplay, or a cathode ray tube (CRT). Additionally, the computer system2000 may include an input device 2012, such as a keyboard, and a cursorcontrol device 2014, such as a mouse. The computer system 2000 can alsoinclude a disk drive unit 2016, a signal generation device 2022, such asa speaker or remote control, and a network interface device 2008.

In a particular embodiment, as depicted in FIG. 21, the disk drive unit2016 may include a computer-readable medium 2018 in which one or moresets of instructions 2020, e.g., software, can be embedded. Further, theinstructions 2020 may embody one or more of the methods or logic asdescribed herein. In a particular embodiment, the instructions 2020 mayreside completely, or at least partially, within the main memory 2004,the static memory 2006, and/or within the processor 2002 duringexecution by the computer system 2000. The main memory 2004 and theprocessor 2002 also may include computer-readable media.

In an alternative embodiment, dedicated hardware implementations, suchas application specific integrated circuits, programmable logic arraysand other hardware devices, can be constructed to implement one or moreof the methods described herein. Applications that may include theapparatus and systems of various embodiments can broadly include avariety of electronic and computer systems. One or more embodimentsdescribed herein may implement functions using two or more specificinterconnected hardware modules or devices with related control and datasignals that can be communicated between and through the modules, or asportions of an application-specific integrated circuit. Accordingly, thepresent system encompasses software, firmware, and hardwareimplementations.

In accordance with various embodiments, the methods described herein maybe implemented by software programs tangibly embodied in aprocessor-readable medium and may be executed by a processor. Further,in an exemplary, non-limited embodiment, implementations can includedistributed processing, component/object distributed processing, andparallel processing. Alternatively, virtual computer system processingcan be constructed to implement one or more of the methods orfunctionality as described herein.

It is also contemplated that a computer-readable medium includesinstructions or receives and executes instructions 2020 responsive to apropagated signal, so that a device connected to a network 2024 cancommunicate voice, video or data over the network 2024. Further, theinstructions 2020 may be transmitted or received over the network 2024via the network interface device 2008.

While the computer-readable medium is shown to be a single medium, theterm “computer-readable medium” includes a single medium or multiplemedia, such as a centralized or distributed database, and/or associatedcaches and servers that store one or more sets of instructions. The term“computer-readable medium” shall also include any medium that is capableof storing, encoding or carrying a set of instructions for execution bya processor or that cause a computer system to perform any one or moreof the methods or operations disclosed herein.

In a particular non-limiting, example embodiment, the computer-readablemedium can include a solid-state memory, such as a memory card or otherpackage, which houses one or more non-volatile read-only memories.Further, the computer-readable medium can be a random access memory orother volatile re-writable memory. Additionally, the computer-readablemedium can include a magneto-optical or optical medium, such as a diskor tapes or other storage device to capture carrier wave signals, suchas a signal communicated over a transmission medium. A digital fileattachment to an e-mail or other self-contained information archive orset of archives may be considered a distribution medium that isequivalent to a tangible storage medium. Accordingly, any one or more ofa computer-readable medium or a distribution medium and otherequivalents and successor media, in which data or instructions may bestored, are included herein.

In accordance with various embodiments, the methods described herein maybe implemented as one or more software programs running on a computerprocessor. Dedicated hardware implementations including, but not limitedto, application specific integrated circuits, programmable logic arrays,and other hardware devices can likewise be constructed to implement themethods described herein. Furthermore, alternative softwareimplementations including, but not limited to, distributed processing orcomponent/object distributed processing, parallel processing, or virtualmachine processing can also be constructed to implement the methodsdescribed herein.

It should also be noted that software that implements the disclosedmethods may optionally be stored on a tangible storage medium, such as:a magnetic medium, such as a disk or tape; a magneto-optical or opticalmedium, such as a disk; or a solid state medium, such as a memory cardor other package that houses one or more read-only (non-volatile)memories, random access memories, or other re-writable (volatile)memories. The software may also utilize a signal containing computerinstructions. A digital file attachment to e-mail or otherself-contained information archive or set of archives is considered adistribution medium equivalent to a tangible storage medium.Accordingly, a tangible storage medium or distribution medium as listedherein, and other equivalents and successor media, in which the softwareimplementations herein may be stored, are included herein.

Thus, a system and method of identifying a source of a heart rhythmdisorder, by identification of rotational of focal activation inrelation to one or more spatial elements associated with the source ofthe heart rhythm disorder, have been described. Although specificexample embodiments have been described, it will be evident that variousmodifications and changes may be made to these embodiments withoutdeparting from the broader scope of the invention. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense. The accompanying drawings that form a parthereof, show by way of illustration, and not of limitation, specificembodiments in which the subject matter may be practiced. Theembodiments illustrated are described in sufficient detail to enablethose skilled in the art to practice the teachings disclosed herein.Other embodiments may be utilized and derived, such that structural andlogical substitutions and changes may be made without departing from thescope of this disclosure. This Detailed Description, therefore, is notto be taken in a limiting sense, and the scope of various embodiments isdefined only by the appended claims, along with the full range ofequivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of any of the above-described embodiments, and otherembodiments not specifically described herein, may be used and are fullycontemplated herein.

The Abstract is provided to comply with 37 C.F.R. §1.72(b) and willallow the reader to quickly ascertain the nature and gist of thetechnical disclosure. It is submitted with the understanding that itwill not be used to interpret or limit the scope or meaning of theclaims.

In the foregoing description of the embodiments, various features aregrouped together in a single embodiment for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting that the claimed embodiments have more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive subject matter lies in less than all features of asingle disclosed embodiment. Thus the following claims are herebyincorporated into the Description of the Embodiments, with each claimstanding on its own as a separate example embodiment.

1. A method for interacting with the human body, the method comprising:detecting bodily signals associated with one or more bodily functions atone or more sensors associated with the human body; processing thebodily signals to create one or more sensed signatures of the one ofmore bodily functions; using signal processing algorithms to tailorsignatures to a given bodily function using an enciphered functionalnetwork in an individual patient to determine one or more effectorresponses needed to control a bodily task in the individual patient;delivering via the enciphered functional network one or more effectorsignals, the effector signals based on the one or more effectorresponses; and controlling a bodily task. 2-3. (canceled)
 4. The methodof claim 1 wherein the bodily signals associated with a biologicalfunction are selected from the group consisting of central or peripheralnervous system signals, autonomic nervous system signals, muscularactivity signals, non-neurologic physiological signals, galvanic skinsignals and combinations thereof.
 5. The method of claim 1, wherein thebodily task is a biological function.
 6. The method of claim 1, whereinthe bodily task is control of activity of a machine external to thebody.
 7. The method of claim 1, wherein the bodily task is control ofactivity of a machine on or inside the body.
 8. The method of claim 1,wherein the bodily task is a combination of an external machine andbiological function.
 9. The method of claim 1, wherein the encipheredfunctional network is represented by symbolic code.
 10. The method ofclaim 9, wherein the symbolic code is a cypher.
 11. The method of claim1, wherein the effector signal directs one or more of a mechanical, anelectrical and a computational device.
 12. The method of claim 1,wherein the detecting and the delivering comprise different regions ofthe human body.
 13. The method of claim 1, wherein the detecting and thedelivering comprise identical regions of the human body.
 14. The methodof claim 1, wherein the controlling comprises treating a biologicaldisease or a biological condition.
 15. The method of claim 1, whereinthe controlling comprises enhancing the performance of a bodily taskdirectly.
 16. The method of claim 1, wherein the controlling comprisesenhancing the performance bodily task using an external machine. 17-139.(canceled)
 140. A system for interacting with the human body, the systemcomprising: a processor; a memory storing instructions that, whenexecuted by the processor, performs operations comprising: detectingbodily signals associated with one or more bodily functions at one ormore sensors associated with the human body; processing the bodilysignals to create one or more sensed signatures of the one of morebodily functions; using signal processing algorithms to tailorsignatures to a given bodily function using an enciphered functionalnetwork in an individual patient to determine one or more effectorresponses needed to control a bodily task in the individual patient;delivering via the enciphered functional network one or more effectorsignals, the effector signals based on the one or more effectorresponses; and controlling a bodily task. 141-172. (canceled)
 173. Themethod of claim 1, wherein the bodily function is selected from thegroup consisting of alertness, biological disease, breathing, cardiacmuscular movement, chronic obstructive pulmonary disease, cognition,driving, memory, mental alertness, mental performance, mood, overallmental performance, patient moving a combined natural/cybernetic limb,physically moving an object, purposeful communication using movement,reading with glasses, response to heart failure, response to obesity,sense of hearing, sense of smell, sense of touch, sense of vision,sensory-motor activities, skeletal muscular movement, sleep, sleepapnea, sleep control, sleep disordered breathing, sleep-breathingconditions, using remote control unit and combinations thereof.
 174. Themethod of claim 1, wherein said one or more sensors is selected from thegroup consisting of: (i) solid physical sensors selected from the groupconsisting of FINE, ECG-electrical sensors, EEG-electrical sensors andcombinations thereof, (ii) non-solid physical sensors selected from thegroup consisting of electrostatic creams, sensors for bioimpedance,piezoelectric film sensors, printed circuit sensors, photosensitivefilm, thermosensitive film and combinations thereof, (iii)external-oriented sensors selected from the group consisting of videosensors, infrared sensors, temperature sensors, gas sensors andcombinations thereof, and (iv) combinations thereof.
 175. The method ofclaim 1, wherein said sensor is a biological sensor that senses at leastone selected from the group consisting of photosensitive sensors,galvanometers, transcutaneous or invasive nerve activity (neuralelectrical activity), muscle electrical activity (myopotentials),pressure detectors, thermal detectors, chemical detectors, mechanicalactivity (mechanoreceptors), body pH, skin pH, mouth pH,gastrointestinal pH, genitourinary tract pH, enzymatic profile, DNAprofile, heart rate, and ventilating (breathing) rate.
 176. The methodof claim 1, wherein said sensor is an external sensor that sensesbiological signals from the nervous system, from another individual'snervous system or from a database of signals.
 177. The method of claim1, wherein said sensor is an external sensor that provides informationselected from the group consisting of pressure, physical movement,temperature, chemical, sound within the normal human physiologicalrange, sound outside the normal human physiological range, sound withinthe physiological range of animals, electromagnetic radiation in thevisible spectrum, electromagnetic radiation in the invisible spectrum,gamma radiation, X-rays, radiowaves, toxins, carbon monoxide, excessivecarbon dioxide, radiation, alpha radiation, beta radiation, biotoxins,toxins of E. coli, and anthrax.
 178. The system of claim 140, whereinthe bodily function is selected from the group consisting of alertness,biological disease, breathing, cardiac muscular movement, chronicobstructive pulmonary disease, cognition, driving, memory, mentalalertness, mental performance, mood, overall mental performance, patientmoving a combined natural/cybernetic limb, physically moving an object,purposeful communication using movement, reading with glasses, responseto heart failure, response to obesity, sense of hearing, sense of smell,sense of touch, sense of vision, sensory-motor activities, skeletalmuscular movement, sleep, sleep apnea, sleep control, sleep disorderedbreathing, sleep-breathing conditions, using remote control unit andcombinations thereof.
 179. The system of claim 140, wherein said one ormore sensors is selected from the group consisting of: (i) solidphysical sensors selected from the group consisting of FINE,ECG-electrical sensors, EEG-electrical sensors and combinations thereof,(ii) non-solid physical sensors selected from the group consisting ofelectrostatic creams, sensors for bioimpedance, piezoelectric filmsensors, printed circuit sensors, photosensitive film, thermosensitivefilm and combinations thereof, (iii) external-oriented sensors selectedfrom the group consisting of video sensors, infrared sensors,temperature sensors, gas sensors and combinations thereof, and (iv)combinations thereof.
 180. The system of claim 140, wherein said sensoris a biological sensor that senses at least one selected from the groupconsisting of photosensitive sensors, galvanometers, transcutaneous orinvasive nerve activity (neural electrical activity), muscle electricalactivity (myopotentials), pressure detectors, thermal detectors,chemical detectors, mechanical activity (mechanoreceptors), body pH,skin pH, mouth pH, gastrointestinal pH, genitourinary tract pH,enzymatic profile, DNA profile, heart rate, and ventilating (breathing)rate.
 181. The system of claim 140, wherein said sensor is an externalsensor that senses biological signals from the nervous system, fromanother individual's nervous system or from a database of signals. 182.The system of claim 140, wherein said sensor is an external sensor thatprovides information selected from the group consisting of pressure,physical movement, temperature, chemical, sound within the normal humanphysiological range, sound outside the normal human physiological range,sound within the physiological range of animals, electromagneticradiation in the visible spectrum, electromagnetic radiation in theinvisible spectrum, gamma radiation, X-rays, radiowaves, toxins, carbonmonoxide, excessive carbon dioxide, radiation, alpha radiation, betaradiation, biotoxins, toxins of E. coli, and anthrax.
 183. The system ofclaim 140, wherein the processor is selected from the group consistingof a central processing unit (CPU), a graphics-processign unit (GPU) anda combination thereof.
 184. The system of claim 140, wherein the bodilysignals associated with a biological function are selected from thegroup consisting of central or peripheral nervous system signals,autonomic nervous system signals, muscular activity signals,non-neurologic physiological signals, galvanic skin signals andcombinations thereof.
 185. The system of claim 140, wherein the bodilytask is selected from the group consisting of a biological function,control of activity of a machine external to the body, control ofactivity of a machine on or inside the body, a combination of anexternal machine and biological function and combinations thereof. 186.The system of claim 140, wherein the enciphered functional network isrepresented by symbolic code.
 187. The system of claim 186, wherein thesymbolic code is a cypher.